ArticlePDF AvailableLiterature Review

Oromotor skills in autism spectrum disorder: A scoping review

April 2023
Autism Research 16(5)

April 2023
16(5)

DOI:10.1002/aur.2923

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CC BY-NC 4.0

Authors:

Marc Maffei

Boston University

Karen Chenausky

MGH Institute for Health Professions

Simone V Gill

Boston University

Helen Tager-Flusberg

Boston University

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Oromotor functioning plays a foundational role in spoken communication and feeding, two areas of significant difficulty for many autistic individuals. However, despite years of research and established differences in gross and fine motor skills in this population, there is currently no clear consensus regarding the presence or nature of oral motor control deficits in autistic individuals. In this scoping review, we summarize research published between 1994 and 2022 to answer the following research questions: (1) What methods have been used to investigate oromotor functioning in autistic individuals? (2) Which oromotor behaviors have been investigated in this population? and (3) What conclusions can be drawn regarding oromotor skills in this population? Seven online databases were searched resulting in 107 studies meeting our inclusion criteria. Included studies varied widely in sample characteristics, behaviors analyzed, and research methodology. The large majority (81%) of included studies report a significant oromotor abnormality related to speech production, nonspeech oromotor skills, or feeding within a sample of autistic individuals based on age norms or in comparison to a control group. We examine these findings to identify trends, address methodological aspects hindering cross-study synthesis and generalization, and provide suggestions for future research.

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REVIEW ARTICLE

Oromotor skills in autism spectrum disorder: A scoping review

Marc F. Maffei

| Karen V. Chenausky

1,2

| Simone V. Gill

Helen Tager-Flusberg

| Jordan R. Green

1,5

Department of Communication Sciences and

Disorders, MGH Institute of Health

Professions, Boston, Massachusetts, USA

Neurology Department, Harvard Medical

School, Boston, Massachusetts, USA

College of Health and Rehabilitation Sciences,

Sargent College, Boston University, Boston,

Massachusetts, USA

Department of Psychological and Brain

Sciences, Boston University, Boston,

Massachusetts, USA

Speech and Hearing Biosciences and

Technology Program, Harvard University,

Cambridge, Massachusetts, USA

Correspondence

Jordan R. Green, MGH Institute of Health

Professions, 36 First Avenue, Boston, MA

02129-4557, USA.

Email: jgreen2@mghihp.edu

Funding information

National Institute on Deafness and Other

Communication Disorders, Grant/Award

Numbers: P50 DC018006, F31 DC020108, R00

DC017490, K24 DC016312

Abstract

Oromotor functioning plays a foundational role in spoken communication and

feeding, two areas of significant difficulty for many autistic individuals. However,

despite years of research and established differences in gross and fine motor skills

in this population, there is currently no clear consensus regarding the presence or

nature of oral motor control deficits in autistic individuals. In this scoping review,

we summarize research published between 1994 and 2022 to answer the following

research questions: (1) What methods have been used to investigate oromotor

functioning in autistic individuals? (2) Which oromotor behaviors have been

investigated in this population? and (3) What conclusions can be drawn regarding

oromotor skills in this population? Seven online databases were searched resulting

in 107 studies meeting our inclusion criteria. Included studies varied widely in

sample characteristics, behaviors analyzed, and research methodology. The large

majority (81%) of included studies report a significant oromotor abnormality

related to speech production, nonspeech oromotor skills, or feeding within a sam-

ple of autistic individuals based on age norms or in comparison to a control

group. We examine these findings to identify trends, address methodological

aspects hindering cross-study synthesis and generalization, and provide sugges-

tions for future research.

Lay Summary

Autistic individuals demonstrate movement abnormalities during tasks including

walking, balancing, reaching, and tool use. However, little is known about move-

ments of the mouth despite well-known difficulties with speech and feeding in

ASD. Here we (1) summarize studies related to mouth movements in ASD, show-

ing that 81% of these studies indicate significant abnormalities in autistic individ-

uals, (2) discuss common findings in these studies, and (3) make suggestions for

future research.

KEYWORDS

autism spectrum disorder, feeding behavior, motor skills, speech disorders, speech production

measurement

INTRODUCTION

Autism spectrum disorder (ASD) is a developmental dis-

ability defined by persistent deficits in social communica-

tion and interaction and by restricted, repetitive patterns

of behavior, interests, or activities (American Psychiatric

Association, 2013). While these core symptoms often

manifest as stereotyped or repetitive movements, motor

impairments themselves are not considered a diagnostic

marker of ASD. Nonetheless, the presence of motor

abnormalities among children with ASD is well estab-

lished, including in infancy (West, 2019), and some have

argued that motor impairments constitute a core feature

of ASD (Fournier et al., 2010; Hilton et al., 2012).

Received: 22 February 2023 Accepted: 15 March 2023

DOI: 10.1002/aur.2923

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any

medium, provided the original work is properly cited and is not used for commercial purposes.

Autism Research. 2023;1–39. wileyonlinelibrary.com/journal/aur 1

Prevalence estimates of motor impairment in ASD are

reported as high as 35%–100% (D. Green et al., 2002;

Licari et al., 2020), and these impairments appear to be

significantly underdiagnosed (Licari et al., 2020). The

reported deficits impact both gross and fine motor skills

and include hypotonia, postural instability, and impair-

ments related to balance, gait, object control, manual

dexterity, repetitive hand and foot movements, precision

grip, and handwriting (Freitag et al., 2007; Garrido

et al., 2017; Gong et al., 2020; Jansiewicz et al., 2006;

Kushki et al., 2011; Ming et al., 2007; Miyahara

et al., 1997; Pan et al., 2009). From a motor control per-

spective, there is evidence suggesting that these deficits

result from difficulties with motor planning (Dewey

et al., 2007; Glazebrook et al., 2006; Mari et al., 2003),

motor execution (Glazebrook et al., 2009; Rinehart

et al., 2006), and the integration of sensory information

(Cascio et al., 2012). There is growing interest in under-

standing motor functioning among individuals with ASD

since motor impairments are associated with full-scale IQ

(Klupp et al., 2021), executive functioning (Michel

et al., 2011), and communicative development (Alcock &

Krawczyk, 2010; Bedford et al., 2016; Gernsbacher

et al., 2008; West, 2019) in this population.

Oromotor skills (i.e., motor skills involving the man-

dible, lips, lower face, soft palate, and/or tongue) have

received relatively little attention in ASD, which reflects

a similar paucity of developmental oromotor research

across populations including typically developing chil-

dren and children with other developmental disabilities.

The functional significance of oromotor skills, which pro-

vide a foundation for behaviors including sucking, chew-

ing, vocalizing, babbling, and speaking, cannot be

overstated. However, many questions remain unanswered

regarding oromotor skills in ASD, including whether or

when adult-like levels of oromotor function are achieved,

how to best assess these skills, how to categorize and

interpret observed abnormalities, and whether these skills

are linked to the development of other outcomes such as

social functioning, nonverbal IQ, ASD symptom severity,

and expressive and receptive language.

Oromotor development and function

Typical oromotor development involves the refinement

of a variety of functional movements over time, begin-

ning as early as 12 weeks after gestation when sucking

behaviors can be observed via ultrasound (de Vries

et al., 1982). Precursors to vocal imitation are observed

within days after birth (Chen et al., 2004; Meltzoff &

Moore, 1983) and the development of adult-like oromo-

tor skills continues past age 10 (see Kent &

Vorperian, 1995). There is little information available

regarding the developmental course of oromotor skills in

ASD, although impairments of oral imitation have been

reported as early as 26 months (Rogers et al., 2003), and

oromotor differences remain through adulthood

(Kissine & Geelhand, 2019; Shriberg et al., 2001). Fur-

thermore, younger siblings of children diagnosed with

ASD—who themselves have a heightened likelihood of

receiving an ASD diagnosis (Zwaigenbaum

et al., 2007)—often demonstrate delayed onset of devel-

opmental motor milestones (Iverson & Wozniak, 2007)

and significant differences in fine motor skills at 12, 24,

and 36 months compared to typically developing controls

(Garrido et al., 2017), suggesting that similar deficits may

be manifested in the oromotor system.

How oromotor function is assessed

Oromotor function is assessed using a variety of methods,

which each provide relative advantages for addressing

particular research questions. Perceptual methods include

visual-perceptual assessments of structure and function

(e.g., an oral mechanism examination) and auditory-

perceptual assessments of tasks such as the repetition of

speech sounds, spontaneous speech production, and max-

imum performance tasks (e.g., diadochokinetic rates).

Perceptual assessment approaches provide ecologically

valid data that are often relatively simple to acquire,

although typically require extensive training, are suscepti-

ble to multiple sources of rater bias (Kent, 1996), and

may be too coarse and/or unreliable to detect subtle dif-

ferences in motor function (Green, 2015). Instrumental

assessment approaches such as acoustic (i.e., based on

sound signals) and kinematic (i.e., based on movement

signals) analyses, on the other hand, provide highly

detailed and reliable data that offer valuable insights into

the physiologic development and performance of the oro-

motor system. However, these methods often require spe-

cialized equipment and may be time-consuming to

analyze and interpret. Various perceptual and instrumen-

tal methods have been used to assess oromotor function-

ing in ASD, but these methods have not been

summarized and reported in prior literature, making it

difficult to assess the quality and type of information

available regarding this issue.

What is assessed?

Speech

Speech production involves the fine coordination of over

100 muscles across multiple systems responsible for respi-

ration, phonation, resonance, and articulation

(Duffy, 2019). Many aspects of speech are studied in typi-

cally developing children to characterize normal develop-

ment and function, and in atypical populations as a tool

for clinical stratification, progress monitoring, and the

prediction of developmental outcomes.

Perhaps the most widely reported characteristic of

speech is articulatory accuracy assessed via auditory-

perceptual evaluation. Analysis of speech errors can offer

2MAFFEI ET AL.

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important information about the presence and nature of

a motor speech disorder (Pernon et al., 2022), including

for differential diagnosis (Allison et al., 2020). Other

approaches to assessing the overall adequacy of speech

production include measuring intelligibility (i.e., the

degree to which the speech signal is understood by a lis-

tener) (e.g., Kent et al., 1989) and phonetic inventory

(i.e., the number of speech sounds produced correctly)

(e.g., Chenausky et al., 2018), which can be used to indi-

rectly assess the capabilities of the oral motor system.

Other aspects of speech production that are com-

monly assessed include temporal features like the dura-

tion of speech sounds and the rate of speech production,

which can both provide important information about

motor planning and execution abilities. Specific charac-

teristics of the speech signal available via acoustic

analysis—including formants (i.e., resonant frequencies

corresponding to vocal tract configurations) (see Kent &

Vorperian, 2018 for a review) and voice onset time

(i.e., the length of time between the release of a stop con-

sonant and the onset of voicing) (Allen et al., 2003)—

allow examination of the speed, consistency, and coordi-

nation of the speech motor system. Kinematic methods

(e.g., electromagnetic articulography or video-based

movement tracking) provide highly detailed data regard-

ing movement velocity, acceleration, and magnitude

(Nip, 2012; Yunusova et al., 2010), from which

researchers can derive information including the level of

coordination among articulators (J. R. Green

et al., 2002) and the spatiotemporal stability of speech

movements (Smith et al., 1995). Speech motor function

may also be investigated more directly via real-time

recordings of muscle activations via surface electromyog-

raphy (EMG; e.g., McClean & Tasko, 2003) and imaging

of brain activity, such as with electroencephalography

(EEG; e.g., Janssen et al., 2020), positron emission

tomography (PET; e.g., Price, 2012), functional magnetic

resonance imaging (fMRI; e.g., Price, 2012), and magne-

toencephalography (MEG; e.g., Heinks-Maldonado

et al., 2006).

Nonspeech oromotor skills

Nonspeech oromotor skills are also commonly assessed,

including lateralizing and protruding the tongue, spread-

ing and puckering the lips, puffing the cheeks, and open-

ing and closing the mouth. Assessment of these tasks is

useful for several reasons. First, it can provide valuable

information about the motor functioning of the articula-

tors in isolation, since speakers may be able to compen-

sate for impairments in one articulator by adapting the

performance of another during speech (Yunusova

et al., 2008). Second, the motor control of speech is

domain-specific, characterized by functional neurological

organization attuned specifically for the generation of

speech sounds (Ziegler & Ackermann, 2013). Therefore,

the assessment of single articulators in nonspeech con-

texts independent of influences from the linguistic system

provides unique information about oromotor function

(Ballard & Robin, 2002). Third, nonspeech oromotor

imitation is particularly useful in the assessment of chil-

dren with ASD, up to one-third of whom remain mini-

mally verbal by school age (Tager-Flusberg &

Kasari, 2013) and therefore have significant difficulty

providing speech samples for evaluation. Nonspeech oro-

motor skills are most commonly assessed visuo-

perceptually via an oral mechanism examination or stan-

dardized test (e.g., the Oral Speech Mechanism Screening

Examination; St. Louis & Ruscello, 2000). Alternatively,

nonspeech oromotor behaviors such as spontaneous

motility of the articulators have been assessed kinemati-

cally (Green & Wilson, 2006).

Feeding

Despite the significant functional impacts of speech

development, the most critical function of the oromotor

system is feeding. There is much research interest in the

feeding behaviors of children diagnosed with ASD since

they are five times more likely to experience feeding prob-

lems compared to their peers (Sharp et al., 2013), leading

to a high rate of nutritional deficiencies (Cornish, 1998).

The most commonly reported difficulties among children

with ASD are restricted food preferences related to sen-

sory aversions, although a limited body of research has

investigated feeding-related motor impairments in ASD.

Methods of assessment have included perceptual observa-

tion as part of an oromotor assessment battery (Amato &

Slavin, 1998), observational studies of meals (Peterson

et al., 2016), and EMG measurements of muscle activity

during a feeding action (e.g., Cattaneo et al., 2007).

Potential correlations between oromotor and

other skills in ASD

Oromotor skills may serve as valuable predictors of other

skills in ASD, such as expressive and receptive language,

social functioning, and nonverbal IQ. Valid predictors of

language outcomes in ASD are critical since the mecha-

nisms that underlie severe language impairments in ASD

remain unclear and such knowledge would directly

inform the development of high-quality language inter-

ventions. Predictors of other variables such as ASD

symptom severity, nonverbal IQ, and social functioning

are also critically needed, since the assessment of such

skills currently requires waiting until the typical age of

emergence of skills like symbolic play, spoken language,

and direction following, leading to a delay in identifica-

tion of impairments. There is promising evidence that

oromotor skills can act as a predictor for these skills; for

instance, some studies show that oromotor skills are

MAFFEI ET AL.3

predictive of expressive language (e.g., Amato &

Slavin, 1998; Gernsbacher et al., 2008), including in mini-

mally verbal autistic individuals (Chenausky et al., 2019).

Aims of this scoping review

Due to a paucity of systematic research regarding oromo-

tor skills in ASD, it is currently unclear which oromotor

skills have been investigated in ASD, what methods have

been used to investigate these skills, and what generaliza-

tions (if any) can be made from existing findings. Indeed,

there does not even appear to be a consensus on whether

oromotor skills are impacted in this population. For

instance, while some research suggests that oromotor

skills such as articulation are relatively spared among

autistic individuals (Dalton et al., 2017; Kjelgaard &

Tager-Flusberg, 2001), other studies report overt abnor-

malities in oromotor functioning such as reduced speech

accuracy, longer sound durations, reduced consistency of

speech production, and decreased oromotor control dur-

ing nonspeech tasks (Adams, 1998; Chenausky

et al., 2019; Gernsbacher et al., 2008; Kissine &

Geelhand, 2019).

For these reasons, a fuller understanding of the oro-

motor skills of autistic individuals is warranted. Such

findings have the potential to advance efforts to improve

the early detection of speech and language deficits in

ASD, support the use of motor- or articulation-based

therapies in conjunction with language-based approaches

for autistic individuals, and identify neurobiological

mechanisms influencing communication development.

The purpose of this scoping review is to summarize and

disseminate existing research concerning oromotor skills

among autistic individuals—including research correlat-

ing these skills to other outcomes—and to address meth-

odological aspects of these studies that hinder cross-study

synthesis and generalization to the population. The long-

term goal is to motivate future investigations into the

presence, nature, and severity of oromotor impairments

in ASD and the association of such deficits with other

functional skills.

METHODS

This review follows the framework for a scoping review

outlined by Arksey and O’Malley (2005), who provided

guidelines that were used to structure the methodology

reported below. The review also adhered to the PRISMA

guidelines for scoping reviews (Tricco et al., 2018).

Research questions

To summarize and disseminate research findings and to

identify research gaps in the existing body of literature,

we addressed the following three research questions:

(1) What methods have been used to investigate oromo-

tor functioning among individuals with ASD?, (2) Which

oromotor behaviors have been investigated?, and

(3) What conclusions can be drawn regarding oromotor

skills in ASD?

Search procedures

In consultation with a health sciences librarian, a com-

prehensive search was conducted of the following data-

bases: Cumulative Index to Nursing and Allied Health

Literature (CINAHL), Education Resources Information

Center (ERIC), MEDLINE, ProQuest Dissertations &

Theses Database (PQDT), PsychInfo, ScienceDirect, and

Web of Science. Studies pertinent to the outlined research

questions were found by searching databases using three

combined categories of carefully selected keywords

(i.e., autism, motor, and oromotor). The search terms

used were: [autis* AND (motor OR articulation) AND

((oral OR oral* OR oro*) OR feeding OR chewing OR

(nonspeech OR “nonspeech”) OR speech OR articula-

tion)]. Search syntax was adjusted as necessary to fit the

requirements of specific databases.

To be included in the review, a study had to meet the

following inclusion criteria: (1) subject-specific or aggre-

gate data that applied only to individuals with ASD

(of any age) could be extracted, (2) the diagnosis of ASD

was made using an established tool (e.g., the Autism

Diagnostic Observation Schedule [ADOS] or the Child

Autism Rating Scale [CARS]) according to DSM-IV or

DSM-5 criteria (and thus published no earlier than 1994),

(3) quantitative data from a behavioral measure

(i.e., perceptual or instrumental), parent report, or medi-

cal record review of oromotor function was reported, and

(4) the study was available in English. A study was

excluded from the review if it met any of the following

exclusion criteria: (1) the main diagnosis of the ASD

participant(s) was a known syndrome (e.g., Fragile X

Syndrome, Prader-Willi Syndrome), (2) a comorbid diag-

nosis with potential to explain oromotor differences

(e.g., hearing impairment, cerebral palsy) was reported,

or (3) the study was conducted using an animal model.

A total of 16,399 studies were collected for screening

(Figure 1). After removing 3855 duplicates that were

included in the results from more than one database,

12,544 articles were left to consider. The first author

(MFM) read the titles and, as necessary, the abstracts of

all remaining articles to remove irrelevant articles

(i.e., violated exclusionary criteria within the title or

abstract). To assess reliability, the second author (KVC)

screened the titles and abstracts of 1500 articles, and the

inter-rater reliability was 75%. The first author made the

final decision on disagreements during this stage, and

776 studies that could not be excluded based on the title

and abstract screening were selected for full-text review.

The first and second authors independently read 370 of

these studies and achieved an inter-rater agreement of

4MAFFEI ET AL.

80%, settling disagreements at this stage by discussion.

The first author then reviewed the additional 406 studies.

669 studies were excluded during this stage, with the most

common reasons being lack of quantitative oromotor

data (512 studies), no ASD-specific findings (34 studies),

diagnosis of ASD not made by an approved instrument

(32 studies), and being a review paper (18 studies). Rea-

sonable effort was made to contact authors or their asso-

ciated organizations when papers were unavailable. A

total of 107 research papers, book chapters, and theses

were ultimately included in the review.

For this scoping review, we define oromotor as referring

to skills involving the use of the mandibular, labial, facial,

velar, and lingual structures for speech, nonspeech oral

movements, and feeding. Studies specific to breath support

or phonatory control (e.g., vocal loudness, average funda-

mental frequency, pitch variation during lexical stress tasks,

etc.) were excluded. However, studies investigating the

coordination of the articulatory and phonatory subsystems

(e.g., measures of voice onset time) were included since they

involve oral structures in addition to laryngeal structures.

Studies addressing prosody cannot be disregarded when

considering the connection between speech and language

(for a review of this topic see Loveall et al., 2021) but were

generally considered outside the scope of this review unless

the duration of speech sounds was reported (e.g., Diehl &

Paul, 2013; Grossman et al., 2010; Van Santen et al., 2010).

Studies involving infant siblings of children with ASD

(who have an elevated likelihood of receiving an ASD diag-

nosis) were only included if a valid ASD diagnosis was later

made and reported in the study. Finally, studies that looked

primarily at phonological skills were excluded unless some

information about oromotor functioning could be derived

from the results.

The following data, if reported, were extracted from

each included study and entered into a Microsoft Excel

spreadsheet: author(s), title, year of publication, journal,

sample size, number of male and female subjects, control

group type, participant ages, diagnosis method, NVIQ,

expressive language skills, behavior(s) analyzed

(e.g., accuracy, duration, etc.), test(s) used, speech unit(s)

analyzed (i.e., phoneme, syllable, phrase/sentence, and/or

connected/spontaneous speech), domain(s) investigated

(i.e., speech, nonspeech, and/or feeding), data type

(i.e., perceptual, instrumental, report/questionnaire,

and/or record review), and results. Because this is a scop-

ing review of existing studies, it did not require ethics

committee approval.

RESULTS

The studies included in this review were heterogeneous in

terms of their aims, sample characteristics, and method-

ology. Table 3provides a summary of the included litera-

ture including a summary of relevant results.

Aims of included studies

Of the 107 studies included in this scoping review, some

were explicitly designed to investigate motor speech skills

(21 studies; 20%) or nonspeech oromotor skills (six stud-

ies; 6%), or motor aspects of feeding (one study; 1%).

However, the majority of studies included oromotor skills

as descriptive statistics or an outcome measure while

investigating other areas including general eating distur-

bances (nine studies; 8%), prosody (13 studies; 12%), gen-

eral speech sound production skills (13 studies; 12%),

intervention outcomes (11 studies; 10%), language skills

(six studies, 6%), general motor functioning (five studies,

5%), and a variety of other areas (22 studies; 20%) includ-

ing imitation abilities, early features of ASD, and inten-

tion understanding.

FIGURE 1 PRISMA flow

diagram summarizing the study

identification process.

MAFFEI ET AL.5

Sample characteristics

ASD diagnosis

A valid, documented method of ASD diagnosis or confir-

mation of diagnosis was an inclusion criterion for this

review to ensure that findings pertain specifically to autistic

children (i.e., to accurately characterize participants). The

Autism Diagnostic Observation Schedule (ADOS or

ADOS-2; Lord et al., 2012) was used to confirm an ASD

diagnosis in 58 studies (54%); the Autism Diagnostic Inter-

view, Revised (ADI-R; Rutter, Le Couteur, & Lord, 2003)

was used in 15 studies (14%); the Child Autism Rating

Scale (CARS; Schopler et al., 1986) was used in 14 studies

(13%); the Social Communication Questionnaire (SCQ;

Rutter, Bailey, & Lord, 2003) was used in 10 studies (9%);

and the Children’s Communication Checklist (CCC;

Bishop, 1998) was used in two studies (2%). Assessment

tools used in a single study were the Autism Spectrum Rat-

ing Scales (ASRS; Goldstein & Naglieri, 2009), the Social

Responsiveness Scale (SRS; Constantino & Gruber, 2005),

the Gilliam Autism Rating Scale (GARS; Gilliam, 2006),

and the Screening Tool for Autism in Toddlers and Young

Children (STAT; Stone et al., 2004).

Fifty-seven studies (53%) made specific reference to the

Diagnostic and Statistical Manual of Mental Disorders

(DSM-IV or -5; American Psychiatric Association, 2013)

criteria for diagnosis of ASD, and eight studies (7%)

referred to the World Health Organization’s International

Classification of Diseases (ICD; World Health

Organization, 2004). The remaining 42 studies (39%)

reported only the tool and not the classification system used

for diagnosis or confirmation of diagnosis.

Sample size

The number of autistic subjects included in each study ran-

ged from one (Biller et al., 2022; Biller & Johnson, 2020;

Kim & Seung, 2015; Petinou, 2021) to 294 (Nakaoka

et al., 2022), with nine additional papers including over one

hundred autistic subjects (Demartini et al., 2021;Gal

et al., 2022; Gernsbacher et al., 2008; Leader et al., 2020;

Mandelbaum et al., 2006; Manfredonia et al., 2019;

Parmeggiani et al., 2019; Vashdi, 2014; Vissoker

et al., 2019). Eighteen studies (17%) included a sample of

fewer than 10 autistic subjects, 27 studies (25%) had a sam-

plesizeof10–20 autistic subjects, 24 studies (22%) had a

sample size of 21–30 autistic subjects, 13 studies (12%) had

asamplesizeof31–50, and 25 studies (23%) had a sample

size of over 50 autistic subjects.

Age range

Means, standard deviations, and ranges of autistic sub-

jects’ages were extracted from each paper. Among the

studies in which mean age was reported or could be cal-

culated (n=96), nine studies (9%) had a mean subject

age of 0–2.9 years, 23 studies (24%) had a mean subject

age of 3.0–5.9 years, 25 studies (26%) had a mean subject

age of 6.0–8.9 years, 15 studies (16%) had a mean subject

age of 9.0–11.9 years, 11 studies (11%) had a mean sub-

ject age of 12.0–17.9 years, and 13 studies (14%) had a

mean subject age of 18 years or older. Figure 2provides

a visual representation of the mean subject age and, when

possible, range in years.

Male-to-female ratio

The male-to-female ratio of children meeting criteria for

ASD is often cited to be approximately 4:1, although a

systematic review and meta-analysis of prevalence studies

found that the ratio may be closer to 3:1 (Loomes

et al., 2017). Many studies included in the current scoping

review included both male and female subjects and

reported the number of each (n=79), allowing the calcu-

lation and comparison of male-to-female ratios.

The smallest male-to-female ratio was 0.9:1 (Lundin

Remnélius et al., 2022), the only study with more female

than male autistic subjects. The male-to-female ratios of

the remaining papers ranged from 1:1 (i.e., an equal num-

ber of male and female subjects) to 33:1 (i.e., one female

subject for every 33 male subjects). The median ratio was

4:1, the mode was 4:1, and the mean male-to-female ratio

was 4.8:1. Of the 79 studies included in this male-to-

female ratio analysis, a total of 35 studies (44%) had a

male-to-female ratio between 2:1 and 4:1.

Nonverbal IQ/mental age

Seventy-six studies (71%) reported a mean, range, or

inclusion cutoff value of nonverbal IQ or mental age for

their sample of autistic individuals. There was significant

heterogeneity between and within studies, and the

FIGURE 2 Mean and range of subject ages, ordered by

mean (n=96).

6MAFFEI ET AL.

assessment tools used to measure IQ or mental age were

also varied. Results from a total of 18 different standard-

ized assessment tools were reported, as were various com-

posite scores, subtest scores, and age equivalents from

these tests. See Table 3for IQ data from each included

study.

Expressive language abilities

Sixty-one studies (57%) reported data related to the

expressive language skills of their sample of autistic indi-

viduals, excluding composite language scores measuring

both expressive and receptive skills. Overall, 19 different

standardized assessment tools were used to quantify

expressive language in terms of normative scores or age

equivalents. In addition, other methods derived from nat-

ural language samples were used (e.g., number of differ-

ent words, mean length of utterance, and presence/

absence of productive syntax), particularly in studies

involving minimally verbal autistic children. As with

nonverbal IQ, expressive language scores were heteroge-

neous both within and across studies, and scores are

reported in detail in Table 3.

Control samples

A total of 72 studies (67%) included a control group.

Most control groups were comprised of individuals con-

sidered “typically developing,”“neurotypical,”or simply

“non-ASD”(64 studies; 60% of all studies; 89% of studies

with a control group). Other control samples included

individuals with a developmental delay (six studies; 6%

of all studies), suspected or diagnosed CAS (four studies;

4%), borderline/low IQ or intellectual disability (three

studies; 3%), genetic disorders (three studies; 3%), an ele-

vated likelihood of receiving an ASD diagnosis due to

having an older sibling with ASD (three studies; 3%),

learning disabilities (two studies; 2%), and developmental

language disorder/specific language impairment (two

studies; 2%). Control populations used in a single study

were individuals with a motor speech disorder, a speech

sound disorder, an expressive language disorder, a recep-

tive language disorder, or a traumatic brain injury.

Research question 1: What methods have been

used to investigate oromotor functioning among

individuals with ASD?

Methodologies of included studies

Perceptual studies

Sixty-six studies (62%) used perceptual (i.e., auditory per-

ceptual and/or visual perceptual) methods of assessment.

Of those 66 perceptual studies, 56 (85%) reported a deficit

among autistic individuals on some measure of oromotor

functioning. The remaining 10 perceptual studies (15%)

did not report a significant oromotor impairment among

their ASD sample (Dalton et al., 2017; Demopoulos &

Lewine, 2016; Espanola Aguirre & Gutierrez, 2019;

McCann et al., 2007; McCleery et al., 2006; Nadig &

Shaw, 2011; Noterdaeme, 2002; Sheinkopf et al., 2000;

Shriberg et al., 2019; Yoder et al., 2015).

Interviews/questionnaires

Interviews or questionnaires of autistic individuals or

their caregivers were used in 14 studies (13%; Ashley

et al., 2020; Demartini et al., 2021; Gal et al., 2022;

Gernsbacher et al., 2008; Leader et al., 2020; Lundin

Remnélius et al., 2022; Nakaoka et al., 2022; Shriberg

et al., 2010; Spek et al., 2020; Stevenson et al., 2017;

Thurm et al., 2007; van Dijk et al., 2021; Velleman

et al., 2010; Vissoker et al., 2019). Nine of these studies

(64%) reported a deficit in some measure of oromotor

functioning among their autistic sample. Parent question-

naires used in these studies were the Autism Eating Ques-

tionnaire (AEQ; Vissoker et al., 2019), the Autism

Spectrum Disorder Mealtime Behavior Questionnaire

(ASD-MBQ; Nakaoka et al., 2020); the Behavioral Pedi-

atrics Feeding Assessment Scale (BPFAS; Crist &

Napier-Phillips, 2001), the Montreal Children’s Hospital

Feeding Scale (MCH-FS; Ramsay et al., 2011), the

Screening Tool of Feeding Problems for Children (STEP-

CHILD; Seiverling et al., 2011), the Sequenced Inventory

of Communication Development (SICD; Hedrick

et al., 1975), the SWedish Eating Assessment for Autism

spectrum disorders (SWEAA; Karlsson et al., 2013), and

the item “imitates sounds”from the Vineland Adaptive

Behavior Scales (VABS; Sparrow et al., 1984). Three

studies used non-standardized questionnaires or caregiver

reports: Gernsbacher et al. (2008) and Stevenson et al.

(2017) employed a landmark-based parent interview pro-

cedure designed to aid recollection of children’s motor

skills at specific ages, and Velleman et al. (2010) inter-

viewed parents directly regarding variables associated

with signs of dysarthria and apraxia of speech in their

children’s speech.

Medical record review

Two studies (2%) conducted retrospective reviews of

medical records to identify oromotor deficits among sam-

ples of autistic individuals. These studies identified an

increased prevalence of an absent sucking reflex in

infancy (Parmeggiani et al., 2019) and a high comorbidity

of ASD and the motor speech disorder childhood apraxia

of speech (CAS; Vashdi et al., 2020).

Instrumental studies

Thirty-six studies (34%) utilized instrumental methods of

assessment. Of those 36 studies, 29 (81%) reported a defi-

cit among autistic individuals on some measure of oro-

motor functioning. Twenty-four studies (67% of

instrumental studies) used acoustic analyses, five studies

(14%) used facial motion tracking (Gladfelter &

MAFFEI ET AL.7

Goffman, 2018; Kothare et al., 2021; Manfredonia

et al., 2019; Parish-Morris et al., 2018; Samad

et al., 2019), two studies (5%) used electromyography to

measure muscle activation (Cattaneo et al., 2007;

Pascolo & Cattarinussi, 2012), and two studies (5%) used

magnetic resonance imaging of the brain (Chenausky,

Kernbach, et al., 2017; Heller Murray et al., 2022). Each

of the following methods was used in only one study:

nasometry (Kasthurirathne et al., 2020), magnetoenceph-

alography (Pang et al., 2016), and ultrasound tongue

imaging (McKeever et al., 2022).

Assessment tools used

Less than half of the included studies (n=51, 48%) used

a standardized assessment tool to examine oromotor

function. Of these 51 studies, 33 (65%) used a norm-

referenced assessment tool. The remaining studies relied

on a test under development, a test that had not been

norm-referenced, or a test without readily available psy-

chometric data. Table 1lists the assessment tools used in

the included studies, the overall purpose of the test as

stated by the test developers, and the number of included

studies in which each test was used.

Of the assessment tools used, seven were designed to

assess articulation and/or phonology or phonological

processing (AAPS, CTOPP, DEAP, GFTA, POP,

SWPT, and TPPD), six to assess feeding skills (AEQ,

ASD-MBQ, BPFAS, MGH-FS, STEP-CHILD, and

SWEAA), four to assess the oral speech mechanism

(CDOMA, OSMSE, POME, and VMPAC), two to

assess prosody (PEPS-C and T-TRIP), and one to aid in

the diagnosis of CAS (KSPT). The remaining tests are

assessments of expressive and receptive communicative

abilities or general development that include subtests use-

ful for assessing oromotor skills or were used to elicit spe-

cific types of speech samples for instrumental analysis.

For example, the NEPSY is a broad assessment of neuro-

psychological development that includes the Oromotor

Sequences subtest in which a child repeats articulatory

sequences and tongue twisters. The specific editions of

these tests used in each study are included in Table 3.

Question 2: What oromotor behaviors have been

investigated?

As indicated in Figure 3, of the 107 included studies, 83 stud-

ies (78%) address the domain of speech production,

28 (26%) address nonspeech oral movements, and 19 (18%)

address motor-related feeding issues. Nineteen studies (18%)

addressed more than one of these domains (e.g., speech and

nonspeech oral movements). Specific information regarding

the skills examined in these studies is provided below, orga-

nized by methodology (i.e., perceptual, instrumental, parent

interview/questionnaire, and medical record review) in des-

cending order of frequency.

Perceptual studies

The most common oromotor skill assessed perceptually

was speech accuracy (i.e., whether or not a speech stimu-

lus was produced correctly) (52 of 66 perceptual speech

studies; 79%). Among studies examining speech accuracy,

31 studies (60%) examined the accuracy of phonemes

(i.e., vowels and/or consonants) either in isolation or

within productions of words or nonwords, 19 studies

(37%) examined the accuracy of syllables, four studies

(8%) examined accuracy during the production of phrases

or sentences, and seven studies (13%) examined speech

accuracy during spontaneous or connected speech (e.g., a

picture description task).

Twenty-two perceptual studies (33%) examined the

ability to accurately perform nonspeech oral movements

(Adams, 1998; Akin-Bulbul & Ozdemir, 2022; Amato &

Slavin, 1998; Belmonte et al., 2013; Biller &

Johnson, 2019,2020; Bodison, 2015; Chenausky

et al., 2019; Chenausky & Tager-Flusberg, 2017; Dalton

et al., 2017; Deshmukh, 2012; Espanola Aguirre &

Gutierrez, 2019; Gernsbacher et al., 2008; Kim &

Seung, 2015; Mandelbaum et al., 2006; McDaniel

et al., 2018; Noterdaeme, 2002; Rogers et al., 2003;

Rogers & Pennington, 2021; Tierney et al., 2015;

Velleman et al., 2010; Yoder et al., 2015).

Fourteen (21%) measured phonetic inventory either

in imitation or from a spontaneous speech sample (Biller

et al., 2022; Broome et al., 2021; Chenausky et al., 2016,

2018,2021; Chenausky, Nelson, & Tager-Flusberg, 2017;

Chenausky, Norton, et al., 2022; Chenausky, Norton, &

Schlaug, 2017; Chenausky & Schlaug, 2018; Kim &

Seung, 2015; Landa et al., 2013; Petinou, 2021; Schoen

et al., 2011; Yoder et al., 2015), 11 (17%) examined

speech rate or diadochokinetic rate (DDK; rapidly pro-

duced sequences of syllables) (Chenausky et al., 2019,

2020,2021; Deshmukh, 2012; Mahler, 2012;

Mandelbaum et al., 2006; Nadig & Shaw, 2011; Patel

et al., 2020; Shriberg et al., 2001,2011; Velleman

et al., 2010), seven (11%) examined the consistency or sta-

bility of speech production (Chenausky et al., 2019,2020,

2021; Deshmukh, 2012; Gladfelter & Goffman, 2018;

Mahler, 2012; Shriberg et al., 2011), six (9%) examined

motor-related feeding/eating behaviors (Amato &

Slavin, 1998; Brisson et al., 2012; McDaniel et al., 2018;

Peterson et al., 2016,2019; Yoder et al., 2015), five (8%)

examined speech intelligibility (Gabig, 2008; Koegel

et al., 1998; Lyakso et al., 2017; Petinou, 2021; Shriberg

et al., 2001), five (8%) examined vocalization quality

(Chenausky, Nelson, & Tager-Flusberg, 2017; Plumb &

Wetherby, 2013; Schoen et al., 2011; Sheinkopf

et al., 2000; Trembath et al., 2019), four (6%) examined

resonance quality (Chenausky et al., 2019,2020,2021;

Shriberg et al., 2011), and three (5%) examined coarticu-

lation (Chenausky et al., 2016,2020,2021). Speech sound

duration (Sheinkopf et al., 2000) and motor anticipation

(Brisson et al., 2012) were examined perceptually in one

study each.

8MAFFEI ET AL.

Instrumental studies

Acoustic analysis

Of the 24 studies that used acoustic analysis, 10 studies

(42%) used these methods to measure the duration of

vowels, syllables, or words (Diehl & Paul, 2013;

Grossman et al., 2010; Hubbard & Trauner, 2007;

Kissine & Geelhand, 2019; Lyakso et al., 2016,2017;

Paul et al., 2008; Shriberg et al., 2019; Van Santen

et al., 2010; Velleman et al., 2010), nine studies (38%)

examined vowel formants (i.e., concentrations of acoustic

energy at particular frequencies) (Chenausky et al., 2021;

Kissine et al., 2021; Kissine & Geelhand, 2019; Lyakso

et al., 2016,2017; Shriberg et al., 2019; Sullivan

et al., 2013; Talkar et al., 2020; Velleman et al., 2010),

seven studies (29%) examined speech rate (Ehlen

et al., 2020; Nadig & Shaw, 2011; Ochi et al., 2019; Patel

et al., 2020; Shriberg et al., 2011; Velleman et al., 2010;

Wynn et al., 2018), and five studies (21%) examined the

consistency or stability of speech production (Chenausky

et al., 2021; Kissine et al., 2021; Kissine &

Geelhand, 2019; Shriberg et al., 2011,2019). Each of the

TABLE 1 Tests used to assess oromotor function in included studies.

Abbreviation Test name Test citation Stated purpose

Studies

AAPS Arizona Articulation Proficiency Scale Fudala and Reynolds (1986) Articulation/phonology 1

AEQ Autism Eating Questionnaire Gal et al. (2022) Feeding skills 2

ASD-MBQ Mealtime Behavior Questionnaire for Children

with ASD

Nakaoka et al. (2020) Feeding skills 1

BPFAS Behavioral Pediatrics Feeding Assessment Scale Crist and Napier-Phillips (2001) Feeding skills 1

CDOMA Com DEALL Oro Motor Assessment Archana (2008) Oral motor function 1

CSBS Communication and Symbolic Behavior Scales Wetherby and Prizant (2002) Communication

development

CTOPP Comprehensive Test of Phonological Processing Wagner et al. (2013) Phonological processing 1

DEAP Diagnostic Evaluation of Articulation and

Phonology

Dodd et al. (2002) Articulation/phonology 2

GFTA Goldman Fristoe Test of Articulation Goldman and Fristoe (2015) Articulation/phonology 10

KSPT Kaufman Speech Praxis Test for Children Kaufman (1995) Identification of CAS 9

MCH-FS The Montreal Children’s Hospital Feeding Scale Ramsay et al. (2011) Feeding skills 1

MVIA Motor and Vocal Imitation Assessment Espanola Aguirre and Gutierrez

(2019)

Motor and vocal imitation 1

NEPSY A Developmental Neuropsychological Assessment Korkman et al. (2007) Neuropsychological

development

OSMSE Oral Speech Mechanism Screening Examination St. Louis and Ruscello (2000) Oral structure and function 2

PEPS-C Profiling Elements of Prosody in Speech–

Communication

Peppé and McCann (2003) Prosody 2

POME/

OME

Pre-School Oral Motor Examination Sheppard (1990) Oral motor function 3

POP Polysyllable Preschool Test Baker (2013) Articulation/phonology 2

SICD Sequenced Inventory of Communication

Development

Hedrick et al. (1975) Communication

development

SIPT Sensory Integration and Praxis Tests Ayres (1996) Sensory integration 1

STEP-

CHILD

Screening Tool of Feeding Problems for Children Seiverling et al. (2011) Feeding skills 1

SWEAA SWedish Eating Assessment for Autism Spectrum

Disorders

Karlsson et al. (2013) Feeding skills 4

SWPT Single Word Polysyllable Test Gozzard et al. (2006) Articulation/phonology 1

TOLD-P Test of Language Development–Primary Newcomer and Hammill (1997) Oral language proficiency 1

TPPD Greek Test of Phonetic and Phonological

Development

Levanti et al. (1995) Articulation/phonology 1

T-TRIP Tennessee Test of Rhythm and Intonation Patterns Koike and Asp (1981) Prosody 1

VABS Vineland-II Adaptive Behavior Scales Sparrow et al. (1984) Aiding diagnosis of ID/DD 1

VMPAC Verbal Motor Production Assessment for Children Hayden and Square-Storer

(1999)

Oral motor function 5

Abbreviations: CAS, childhood apraxia of speech; DD, developmental disability; ID, intellectual disability.

MAFFEI ET AL.9

following speech features was examined in a single study:

temporal synchrony with a metronome (Franich

et al., 2020), speech rhythm (Lau et al., 2022), voice onset

time (i.e., the duration of time between the release of a

plosive and the onset of voicing) (Chenausky & Tager-

Flusberg, 2017), coarticulation (Chenausky et al., 2021),

and phoneme accuracy (Chenausky et al., 2021). Two

studies employed novel acoustic analysis techniques:

Talkar et al. (2020) examined correlations among speech

acoustics, videos of facial movements, and handwriting

data to investigate correlations across systems, and Sulli-

van et al. (2013) used spectral analysis to examine three

different timescales of acoustic signals and derive infor-

mation regarding syllabic rhythm, formant transitions,

and place of articulation.

Facial motion tracking

Five studies (14% of instrumental studies) used facial

motion tracking to examine oromotor performance.

Gladfelter and Goffman (2018) used a 3D optical camera

to capture signals from infrared light-emitting diodes

affixed to children’s faces to quantify speech motor sta-

bility during word learning. Markerless facial motion

tracking was used in the remaining studies to quantify lip

aperture, mouth surface area, jaw velocity, and jaw accel-

eration (Kothare et al., 2021); the use of the lips and jaw

in posed facial expressions of emotions (Manfredonia

et al., 2019); movements of the lips and jaw in spontane-

ous facial actions (Samad et al., 2019); and mouth move-

ment diversity (Parish-Morris et al., 2018).

Electromyography (EMG)

Two studies examined electromyographic activity of the

mylohyoid muscle during goal-oriented actions

(Cattaneo et al., 2007; Pascolo & Cattarinussi, 2012).

Magnetic resonance imaging (MRI)

Two studies used MRI to examine the relationship

between white matter tracts and speech improvement

during intervention (Chenausky, Kernbach, et al., 2017)

and intra-subject variability in neural activity during

speech production (Heller Murray et al., 2022) among

autistic individuals.

Magnetoencephalography (MEG)

Pang et al. (2016) used magnetoencephalography to

examine differences in brain activity during the perfor-

mance of increasingly complex oromotor tasks underly-

ing speech production (i.e., a simple oromotor task, a

phoneme production task, and a phonemic sequenc-

ing task).

Nasometry

Kasthurirathne et al. (2020) used a nasometer to quantify

nasalance (i.e., the ratio of nasal resonance and oral reso-

nance during speech) in a group of autistic teenagers.

Ultrasound

McKeever et al. (2022) used ultrasound imaging to exam-

ine the rate, accuracy, and consistency of tongue move-

ments during DDK repetitions.

Questionnaire/parent report

Fourteen studies (13%) used questionnaires or parent

report to investigate oromotor skills. The majority of

these studies (10 studies; 71% of questionnaire/report

studies) investigated motor-related feeding behaviors

(Ashley et al., 2020; Demartini et al., 2021; Gal

et al., 2022; Karlsson et al., 2013; Leader et al., 2020;

Lundin Remnélius et al., 2022; Nakaoka et al., 2022;

Spek et al., 2020; van Dijk et al., 2021; Vissoker

et al., 2019). Four studies (29%) used questionnaires or

parent report to address nonspeech skills (Gernsbacher

et al., 2008; Stevenson et al., 2017; Thurm et al., 2007;

Velleman et al., 2010). Both Gernsbacher et al. (2008)

and Stevenson et al. (2017) prompted parents with retro-

spective questions regarding activities such as puffing

cheeks on request at age 2 years. Thurm et al. (2007)

prompted parents with one item from the SICD regard-

ing a child’s ability to imitate a raspberry/tongue click,

and Velleman et al. (2010) surveyed parents on a variety

of oromotor characteristics including muscular weakness

of the speech mechanism and problems with the tongue

and velum. A questionnaire or parent report was used to

investigate speech production in a single study: Velleman

et al. (2010) surveyed parents of autistic children with

questions regarding speech distortions, abnormal oral

motor postures during speech, and the production of

unclear consonants.

Medical record review

Two studies (2%) used medical record review to investi-

gate oromotor abnormalities. Parmeggiani et al. (2019)

FIGURE 3 Percentage of studies investigating the domains of

speech, nonspeech, and feeding.

10 MAFFEI ET AL.

examined the medical records of autistic children to

investigate putative early features of ASD including the

absence of a sucking reflex in infancy. Vashdi et al.

(2020) examined the medical records of children diag-

nosed with CAS or suspected CAS for the co-occurrence

of an ASD diagnosis.

Question 3: What do these studies tell us about

oromotor skills in ASD?

A variety of results were reported in the included stud-

ies, which are summarized below as well as in Tables 2

and 3. Table 2displays (1) the most frequently exam-

ined behaviors in the included studies, (2) the number

of included studies examining that behavior, (3) the

number and percentage of studies reporting an abnor-

mality for the behavior among autistic subjects, and

(4) and the nature of the abnormality. Table 3provides

detailed information about each study including rele-

vant results.

Speech

Accuracy

The most commonly investigated aspect of speech motor

functioning among the included studies was the accuracy

of oral movements during speech, which was investigated

in 52 of the 107 studies (49%). Accuracy was quantified

using non-standardized perceptual measures

(e.g., percent phonemes correct, judgments of distortion),

standardized tests like the GFTA and KSPT, and acous-

tic techniques. Forty-four of these studies (85%) reported

abnormal accuracy based on normative data or in com-

parison to a control group. In all studies reporting an

abnormality related to speech accuracy, accuracy was

decreased in the ASD group.

Phonetic inventory

Fifteen studies (14%) reported the phonetic inventories of

autistic children, all using auditory-perceptual methods.

Three of these studies included comparisons to a control

group, each of which reported significant differences

related to autistic speakers. Two of these studies reported

a smaller phonetic inventory in an ASD group (Landa &

Garrett-Mayer, 2006; Schoen et al., 2011); Chenausky,

Nelson, and Tager-Flusberg (2017) found no significant

difference in phonetic inventory between toddlers who

were later diagnosed with ASD and those who were not,

although observed a significant Age * Group interaction

in which toddlers with an elevated likelihood of an ASD

diagnosis who did not go on to receive a diagnosis gained

a smaller number of phonemes between 18 and

24 months than non-autistic controls and toddlers who

were later diagnosed with ASD. The remaining 12 studies

(Biller et al., 2022; Broome et al., 2021; Chenausky

et al., 2016,2018,2021; Chenausky, Kernbach,

et al., 2017; Chenausky, Norton, et al., 2022; Chenausky,

Norton, & Schlaug, 2017; Chenausky & Schlaug, 2018;

Kim & Seung, 2015; Petinou, 2021; Yoder et al., 2015)

used phonetic inventory as a baseline measure or out-

come measure without direct comparison to a control

group or normative values.

TABLE 2 Summary of most frequently examined behaviors in included studies.

Behavior # Studies # Abnormal Type of abnormality

Speech

Accuracy 52 44 (85%) Reduced accuracy

Consistency/stability 13 11 (85%) Reduced consistency; increased consistency

Rate 13 7 (54%) Slower rate; less entrainment

Duration 11 9 (82%) Longer duration; less difference in duration between

speaking conditions

Formant values 8 6 (75%) Outcome measures too variable to discern a dominant

pattern

Coordination/coarticulation 6 6 (100%) Reduced coordination; difficulty with coarticulation

Vocalization quality 5 3 (60%) Reduced speechlike vocalizations; increased atypical

vocalizations

Intelligibility 4 3 (75%) Reduced intelligibility

Nonspeech

Nonspeech oral movements 28 20 (71%) Reduced nonspeech oromotor control; differences in

brain activity

Feeding

Eating/feeding skills 19 11 (58%) Reduced oral control for feeding

MAFFEI ET AL.11

TABLE 3 Relevant data from included studies.

Author(s) Domain

Features

nASD (M),

M:F ratio

ASD age M

± SD (range) Controls

NVIQ M±SD

(range)

Exp. lang M± SD (range)

Method

Test

Results

Adams (1998) S/NS Accuracy 4 (3), 3:1 9.0 ± 2.3

(6.3–

11.3)

TD LIPS (92.75 ± 10.5) - P KSPT TD > ASD on oral movements, complex

productions, and total score;

ASD =TD on simple productions

Akin-Bulbul and

Ozdemir (2022)

S/NS Accuracy 30 (22),

2.8:1

2.7 ± 0.3

(2.3–3)

TD, DD BSID-III Cognitive

m=24

- P - TD > ASD on meaningful and

nonmeaningful vocal imitation

Amato and Slavin

(1998)

S/NS/F Accuracy, feeding

difficulty

20 (16), 4:1 (2.5–4.0) - - V and NV P OME Verbal and nonverbal groups had reduced

oromotor functioning. V > NV on

overall score, eating behaviors,

voluntary nonverbal oral ability, and

pre-speech/speech behavior; V =NV

on musculoskeletal anatomy and

basic oral motor functions

Arutiunian et al. (2022) S Accuracy 71 9.6 ± 1.4 (7–

11.1)

TD 83.1 ± 20.5 (40–125) Various P - TD > ASD on nonword repetition,

TD =ASD on word repetition

Ashley et al. (2020) F Feeding difficulty 19 (15),

3.8:1

(1.3–3.0) TD, HR,

non-TD

MSEL-VR (30.9

± 10.9)

MSEL-EL 25.9 ± 8.1 Q BPFAS ASD =TD on oral motor skills for eating

Belmonte et al. (2013) S/NS Accuracy 31 (25),

4.2:1

3.4 ± 0.8

(1.8–5.4)

- - Various P CDOMA 35% of ASD group had an oral motor

impairment associated with an

EL/RL disparity (EL < RL). Oral

motor skills correlated with pre-

intervention RL and EL and with

learning rates. Oral motor skills

varied independently of GM and FM

Biller and Johnson

(2019)

S Accuracy, voicing 5 (4), 4:1 5.3 ± 1.3

(3.6–6.9)

- MSEL-VR AE 1:8–

2:7

MSEL AE 0.9 ± 0.2 P VMPAC MV autistic children below age level on

oromotor control and number of

speech sounds/syllables produced

imitatively or spontaneously

Biller and Johnson

(2020)

S Accuracy, voicing 1 (1) 3.3 - MSEL-NV AE 2:1 MSEL-EL AE 1.8 P VMPAC Oromotor control skills in 5th percentile

Biller et al. (2022) S Inventory, voicing 1 (1) 4.9 - MSEL-VR 2:0 Exp. vocab 25 words P VMPAC Limited sound repertoire

Bodison (2015) NS Accuracy 32 (25),

3.6:1

7.5 ± 1.4 (5–

8.9)

- - - P SIPT Reduced imitative oral praxis

Boorom (2018) S Accuracy 11 (11) 4.9 ± 0.3

(4.3–5.4)

- - TOPEL DV raw 24.1 ± 17.3 P CTOPP-2 Below average group mean for nonword

repetition

Brisson et al. (2012) F Motor

anticipation

13 (13) 0.3 ± 0.1

(0.3–0.5)

TD 8/13 subjects

IQ < 75

- P - TD > ASD on anticipatory mouth

opening in response to an

approaching spoon

Broome et al. (2021) S Accuracy,

inventory

22 (20),

10:1

3.9 ± 1.2 (2–

5.3)

- WPPSI-III 99 ± 20.7 PLS-4 EC 65.6 ± 14.2 (50–85) P POP Cluster analysis identified three

subgroups: high language/high

(Continues)

12 MAFFEI ET AL.

TABLE 3 (Continued)

Author(s) Domain

Features

nASD (M),

M:F ratio

ASD age M

± SD (range) Controls

NVIQ M±SD

(range)

Exp. lang M± SD (range)

Method

Test

Results

speech, low EL/low speech/high RL,

and low language/low speech

Broome et al. (2022) S Inventory 23 (21),

10.5:1

4.4 ± 1.3 (2–

7.2)

- WPPSI-III 99 ± 20.7 PLS-4 EC 65.6 ± 14.2 (50–85) P POP, SWPT Varied speech development trajectories by

subgroup: high language/high speech

and low EL/low speech/high RL

remained stable; low language/low

speech was variable

Cattaneo et al. (2007) F Muscle activity 8 (7), 7:1 6.2 (5.1–9.0) TD WISC-R (98 ± 12.4) - I - TD > ASD on mylohyoid activity while

observing a person eat and while

reaching for/grasping food

Chenausky and Schlaug

(2018)

S Accuracy,

inventory

30 (25), 5:1 6.4 (3.4–9.7) - - EV < 20 words P - Reduced % syllables approximately

correct at baseline

Chenausky and Tager-

Flusberg (2017)

S VOT 11 (7), 1.8:1 2.2 ± 0.6

(1.5–3)

TD, HR- Age 3 MSEL EL 53.6 ± 8.6 P/I - ASD =HR=LRC on VOT mean and

standard deviation. ASD significantly

less likely to produce acoustically

distinct VOT for /b/ and /p/ at

36 months, but not at 18 or

24 months

Chenausky et al. (2016) S Accuracy,

inventory

30 (27), 9:1 1.5 ± 0.5 (1–

- MSEL-MA (21.4

± 9.2) (n=14)

EV < 20 words P/I KSPT Reduced syllables approximated, vowels

correct, and consonants correct at

baseline

Chenausky, Kernbach,

et al. (2017)

S Accuracy,

inventory

30 (25), 5:1 6.3 ± 1.6

(3.4–9.7)

- MSEL-VR (27.9

± 9.7)

MSEL EL 11.2 ± 2.4 (6–19) P - White matter integrity accounted for

significant variance in % syllable-

initial consonants correct, %

responses, and % syllable insertions

Chenausky, Nelson, and

Tager-Flusberg

(2017)

S Inventory 10 (7), 2.3:1 6.3 (3.4–9.7) TD, HR- Age 2 MSEL EL 48.0 ± 9.2 P - Vocalization rate predicted # different

consonants for non-autistic, but not

autistic, children at 12 months and

for both groups at 18 and 24 months.

EL predicted # different consonants

for only the non-autistic children at

18 and 24 months. Mean # different

consonants at 12, 18, and 24 months

was not significantly different

between groups

Chenausky, Norton, and

Schlaug (2017)

S/NS Accuracy,

inventory

4 (4) 5 ± 1.2 (4.1–

6.6)

- MSEL-VR (26.8

± 13.9)

MSEL EL raw 13.3 ± 4.4 P KSPT V > MV on oral movements and simple

phoneme/syllables

Chenausky et al. (2018) S Accuracy,

inventory

38 (31),

4.4:1

6.4 ± 1.6

(3.4–

10.7)

- MSEL-VR (30.1

± 9.6)

MSEL EL 11.1 ± 2.7 I - Reduced % syllables approximately

correct at baseline. Baseline phonetic

inventory significantly predicted

change in % syllables approximately

(Continues)

MAFFEI ET AL.13

TABLE 3 (Continued)

Author(s) Domain

Features

nASD (M),

M:F ratio

ASD age M

± SD (range) Controls

NVIQ M±SD

(range)

Exp. lang M± SD (range)

Method

Test

Results

correct, while nonverbal IQ, baseline

expressive language, and age did not

Chenausky et al. (2019) S/NS Accuracy, rate,

coordination,

consistency,

resonance,

voicing

54 (41),

3.2:1

6.5 ± 1.7

(3.4–

10.7)

- LIPS-3 30–115 (67.4

± 19.5)

NDW 46.3 ± 57.1 P - 24% of sample exhibited severely

disordered speech including >4 signs

of CAS, 30% had speech

abnormalities inconsistent with CAS,

and 24% produced too little speech

for analysis. NS oromotor ability was

not predictive of EL. Speech

imitation predicted # different words

in the sCAS and insufficient speech

groups

Chenausky et al. (2020) S Accuracy, rate,

coordination,

consistency,

resonance,

voicing

27 (24), 8:1 6.6 ± 1.4 (4–

9.7)

CAS MSEL-VR raw

(29.6 ± 9.0)

MSEL EL raw 11.2 ± 1.7 (8–

14)

P GFTA-2 MV autistic children with CAS exhibited

significantly slower rate and more

vowel errors, syllable segmentation,

groping, difficulty with

coarticulation, and additions

compared to a verbal CAS group,

although fewer consonant distortions

Chenausky et al. (2021) S Accuracy, rate,

coordination,

inventory,

consistency,

resonance,

formants

38 (33),

6.6:1

6.8 ± 1.7 (5–

10.7)

- MSEL-VR 30.3

± 9.8

MSEL EL raw 11.3 ± 2.7 (6–

19)

P KSPT A cluster defined by perceptual within-

token variability demonstrated more

within- and between-token variability

and less centralized vowel space than

the low variability group and TD

Chenausky, Norton,

et al. (2022)

S Accuracy,

inventory

14 (11),

3.7:1

10 ± 3.9 (4.3–

18.8)

- LIPS-3 69.1 ± 8.5 EV < 20 words P KSPT Low % syllables, consonants, and vowels

correct; low KSPT scores; small

phonetic inventory

Cleland et al. (2010) S Accuracy 69 9.5 ± 2.2

(5.0–

13.0)

- RPM (103.0 ± 15.0) CELF-3 EL 84.1 ± 20.2 P GFTA-2 12% of sample below average on GFTA,

although 33% in the normal range

presented with errors. Non-

developmental errors observed in

both groups

Dalton et al. (2017) S/NS Accuracy 10 (6), 1.5:1 4.8 ± 0.5 (3–

5.5)

TD, sCAS MSEL-VR AE 4.8

± 1.3

MLU > 3.0 P VMPAC ASD =TD =sCAS on nonverbal oral,

verbal motor, and concurrent verbal

motor imitation. NS oral imitation

and verbal motor imitation were

correlated with joint attention only in

ASD group

Demartini et al. (2021) F Feeding difficulty 106 (77),

2.7:1

33.2 ± 12.9

(17–67)

TD WAIS-IV ≥70 ADOS communication 4.7

± 2.0

Q SWEAA TD > ASD on motor control for eating

S Accuracy 60 (48), 4:1 TD P GFTA-2 ASD =TD on GFTA

(Continues)

14 MAFFEI ET AL.

TABLE 3 (Continued)

Author(s) Domain

Features

nASD (M),

M:F ratio

ASD age M

± SD (range) Controls

NVIQ M±SD

(range)

Exp. lang M± SD (range)

Method

Test

Results

Demopoulos and

Lewine (2016)

10.8 ± 3.4

(5.5–

18.5)

WISC-IV FSIQ 46–

136 (81.7

± 22.0)

CELF-4 EL 75.5 ± 26.6 (45–

128)

Deshmukh (2012) S/NS Accuracy, rate,

consistency

12 (12) 6.5 ± 1.9 (4–

10.3)

TD, MSD - EVT 100.0 ± 14.8 P DEAP,

GFTA-2,

OSMSE-3

ASD group had normal oral structure/

function. 15% of the ASD group

below the normal range on the

GFTA. ASD =TD on rate and

consistency

Diehl and Paul (2013) S Duration 24 (16), 2:1 12.3 ± 2.3

(8.0–

16.0)

TD, LrnD WASI/DAS (103.61

± 17.14)

CELF-IV 100.5 ± 16.2 I PEPS-C ASD > TD on utterance length when

expressing dislike, asking questions,

and making statements. ASD =TD

when expressing like and when using

stress to indicate focus

Ehlen et al. (2020) S Rate 32 (18),

1.3:1

37.1 ± 10.7 TD Incl. criteria >85 - I - ASD =TD on word duration

Espanola Aguirre and

Gutierrez (2019)

S/NS Accuracy 30 (22),

2.8:1

3.6 ± 1.0

(1.3–4.0)

TD MSEL composite

56.6 ± 11.3

MCDI # of words 155.7

± 150.1

P MVIA ASD =TD on vocal and facial imitation

Franich et al. (2020) S Coordination 10 (9), 9:1 (22–31) TD TONI-4 (102.33

± 9.56)

- I - TD > ASD on timing phrases along with

a metronome; ASD had longer time

interval between two consecutive

repetitions of first word in target

phrase

Gabig (2008) S Accuracy,

intelligibility

15 (13),

6.5:1

6.5 ± 0.7 (5–

7.9)

TD DAS 95 ± 10.6 LUL 5.1 ± 2.1 P TOLD-P:3 53% of ASD group below average on an

articulation task

Gal et al. (2022) F Feeding difficulty 105 (87),

4.8:1

3.4 ± 1.3 (3–

7.9)

TD - - Q AEQ TD > ASD on chewing and swallowing

function

Gernsbacher et al.

(2008)

NS Accuracy 115 (92),

1.3:1

7.9 ± 3.7

(2.3–

18.9)

TD - Various P/Q KSPT TD > ASD on parent reports of NS

oromotor skills; parent reports

distinguished autistic children with

minimally, moderately, and highly

fluent speech. Reports of oromotor

and manual motor skills were

correlated. Home videos

corroborated parent reports for 97%

of subjects. During direct assessment,

minimally and highly fluent autistic

children were significantly

distinguished on most NS oral motor

tasks

Gladfelter and Goffman

(2018)

S Accuracy,

consistency

12 (9), 3:1 7.8 ± 1.9

(4.6–

11.3)

TD TONI-4 (96.6

± 6.54)

EVT 95.8 ± 7.6 (79–112) P/I - ASD =TD on oral mechanism exam.

ASD =TD on increase in phonetic

accuracy during word learning.

(Continues)

MAFFEI ET AL.15

TABLE 3 (Continued)

Author(s) Domain

Features

nASD (M),

M:F ratio

ASD age M

± SD (range) Controls

NVIQ M±SD

(range)

Exp. lang M± SD (range)

Method

Test

Results

Although not more stable at baseline,

ASD > TD on gains in stability from

pre- to post-test

Grossman et al. (2010) S Duration 16 12.3 ± 2.3

(7.5–

17.0)

TD KBIT-2 (109.6

± 19.1)

- I - ASD > TD on length of first- and last-

syllable stress items

Heller Murray et al.

(2022)

S Brain activity 15 (12), 4:1 16.7 ± 2.3

(13.8–

21.1)

TD LIPS-3111.9 ± 26.3 NDW/minute 10.3 ± 4.4 (4.9–

20.6)

I - ASD =TD on average speech activation

and inter-subject variability in speech

activation; ASD > TD on intra-

subject neural variability. Intra-

subject variability correlated with

autism severity but not number of

words

Hubbard and Trauner

(2007)

S Duration 18 (6), 2:1 14.5 (6–21) TD - - I - Non-autistic children and children with

AS had longer syllable durations

during sad compared to happy and

angry utterances, children with

autism did not

Karlsson et al. (2013) F Feeding difficulty 57 (38), 2:1 18.7 ± 2.9

(15–25)

TD WISC/WAIS > 70 - Q SWEAA ASD =TD on motor control for eating

Kasthurirathne et al.

(2020)

S Resonance 11 (9), 4.5:1 15.8 ± 1 (14–

17)

TD - Verbally fluent I - ASD > TD on nasalance

Kim (2014) S Accuracy 2 (2) 8.3 ± 1.5

(7.2–9.3)

- - - P - Reduced phoneme accuracy at baseline

Kim and Seung (2015) S/NS Accuracy,

inventory

1 (1) 11 - - EVT-2 AE 3.3 P GFTA-2,

KSPT

Normal NS oromotor skills except

reduced range of lip movement.

Accurately produced all individual

vowels, consonants, syllable types,

repetitive syllables, and simple

monosyllabic words. Age equivalent

of 2 years on the GFTA; atypical

errors and inconsistent error patterns

within and across sessions

Kissine and Geelhand

(2019)

S Consistency,

duration,

formants

38 (26),

2.2:1

28.1 ± 11.5 TD WAIS-4 FSIQ

(112.0 ± 25.8)

- I - TD > ASD on F1–F3 dispersion,

indicating increased articulatory

stability in the ASD group.

ASD > TD on syllable duration

Kissine et al. (2021) S Consistency,

formants

20 (20) 31.6 ± 10.7

(17–52)

TD WAIS-IV 112.06

± 22.8

- I - ASD > TD on articulatory stability; ASD

group’s non-native vowel production

was less accurate and more

influenced by native vowels than TD

group

(Continues)

16 MAFFEI ET AL.

TABLE 3 (Continued)

Author(s) Domain

Features

nASD (M),

M:F ratio

ASD age M

± SD (range) Controls

NVIQ M±SD

(range)

Exp. lang M± SD (range)

Method

Test

Results

Kjelgaard and Tager-

Flusberg (2001)

S Accuracy 89 (80),

8.9:1

7.3 ± 2.4 (4–

13.9)

- DAS (90.1 ± 19.6) CELF-EL 74.86 ± 17.63; EVT

84.89 ± 17.51

P GFTA Average GFTA scores for normal,

borderline, and impaired language

subgroups, although the impaired

group scores were significantly lower

than both other groups

Koegel et al. (1998) S Accuracy,

intelligibility

5 (4), 4:1 5.5 ± 1.4

(3.7–7.5)

- - Various P AAPS Intelligibility ratings ranged from “mostly

not intelligible”to “sometimes

intelligible”

Kothare et al. (2021) S Speed 22 (12),

1.2:1

11.4 ± 2.5 (8–

18)

- WISC-FSIQ 102.95

± 19.79

CELF-5 EL 100.1 ± 20.5 (59–

131)

I - Jaw speed/acceleration correlated with

dominant hand speed

Landa et al. (2013) S Inventory 54 (44),

4.4:1

(0.5–3.5) TD, HRMSEL-C 85.3 ± 19.0 - P - TD > early-ASD group (first clinical

impression by 14 months) on

consonant inventory at 14, 18, and

24 months. TD > late-ASD group on

consonant inventory at 14 and

24 months. Early ASD =late ASD

on consonant inventory

Lau et al. (2022) S Rhythm 57 (48),

5.3:1

16.6 ± 8 (6–

35)

TD WASI/WAIS/WISC-

IV 106.15

± 12.97

- I - TD > ASD on speech rhythm measures

Leader et al. (2020) F Feeding difficulty 136 (98),

2.6:1

8.4 ± 4.1 - - - Q STEP-CHILD 60% of sample reported to have chewing

problems

Lundin Remnélius et al.

(2022)

F Feeding difficulty 28 (13),

0.9:1

20.3 ± 4.4

(15–31)

TD, LrnD,

92.86 ± 21.22 - Q SWEAA Motor control for eating correlated with

internalizing conditions but not

autistic traits

Lyakso et al. (2016) S Duration,

formants

25 (5–14) TD - - I - ASD ≠TD on F3 values during

emotional speech, F2–F1 values for

/a/ and /u/, and F3–F2 values for /i/.

Autistic children who had

developmental reversals at age 1.5–

3 years ≠those at developmental

risk from birth on formant measures

Lyakso et al. (2017) S Duration,

intelligibility,

formants

30 (5–14) TD MA 4:0–7:0 - P/I - TD > ASD on word intelligibility.

Listeners falsely identified male

autistic subjects as female more often

than TD children and tended to

underestimate the age of the autistic

children. Autistic children with a

developmental reversal at age 1.5–

3 years ≠those at developmental

risk from birth on vowel duration

Mahler (2012) S/NS 7 (7) TD - EVT 105.3 P

(Continues)

MAFFEI ET AL.17

TABLE 3 (Continued)

Author(s) Domain

Features

nASD (M),

M:F ratio

ASD age M

± SD (range) Controls

NVIQ M±SD

(range)

Exp. lang M± SD (range)

Method

Test

Results

Accuracy, rate,

consistency

7.4 ± 1.7

(5.1–

10.3)

DEAP,

GFTA-2,

OSMSE-3

TD > ASD on DDK accuracy,

consistency. TD =ASD on DDK

rate. ASD in normal range on GFTA

and OSMSE Structure/Function.

86% failed OSMSE DDK task

Mandelbaum et al.

(2006)

S/NS Accuracy, rate,

neuro exam

116 (93),

4:1

8.9 ± 1.6 (7–

10)

ID, DLD Various - P - Non-ASD/low-IQ > ASD/low-IQ on

oromotor tasks. ASD/high-

IQ > ASD/low-IQ on timed and

untimed oromotor tasks. Per

neurological exam, ASD/low-

IQ > DLD, ASD/low-IQ > ASD/

high-IQ, and non-ASD/low-

IQ > ASD/high-IQ on ratings of

oromotor apraxia

Manfredonia et al.

(2019)

NS Facial expression 144 (112),

3.5:1

14.6 ± 7.8 (6–

54)

TD KBIT-2 99.2 ± 19.6 - I - TD > ASD on upturned lip corner during

happy emotional expression;

activations of lip and jaw correlated

with social skills

McCann et al. (2007) S Accuracy 31 (25),

4.2:1

9.8 (6–13) TD RPM 96.4 ± 15.9 CELF-3 EL 90% below normal

limits

P GFTA-2 Within ASD group, 84% scored within

the normal range, 10% had a mild

impairment and 6% had a more

significant impairment on GFTA

McCleery et al. (2006) S Accuracy 14 (12), 6:1 3.3 (2.1–6.9) TD BSID MA (1:6) MCDI NDW 7 (0–26) P - ASD =TD on patterns of phoneme

acquisition and production

McDaniel et al. (2018) NS/F Accuracy 65 (54),

4.9:1

3.6 ± 0.6

(2.7–4.7)

- MSEL DR 36 ± 15 MCDI NDW 17 ± 25 (0–117) P OME Reduced eating and NS oromotor skills.

Imitative and nonimitative oral

motor performance was not

significantly correlated with

receptive–expressive vocabulary

discrepancy

McKeever et al. (2022) S Accuracy, rate,

consistency

8 (6), 3:1 10.1 ± 2.1

(6.3–

12.5)

TD LIPS 89.71 ± 11.37 - I - ASD =TD on max rate of syllable

production, accuracy of single

syllable sequences, and articulatory

stability. ASD group was more likely

to have different tongue shapes for

fast and slow rates

Nadig and Shaw (2011) S Rate 15 (13),

6.5:1

10.8 ± 1.5

(8.4–

14.4)

TD WASI PIQ 81–126

(105 ± 15)

- P/I - ASD =TD on speech rate, perceptually

and acoustically

Nakaoka et al. (2022) F Feeding difficulty 294 (229),

3.5:1

10 ± 4 (3–18) - - SCQ 12.1 ± 7.3 Q ASD-MBQ Reduced score on oral motor function for

eating. Oral motor function

(Continues)

18 MAFFEI ET AL.

TABLE 3 (Continued)

Author(s) Domain

Features

nASD (M),

M:F ratio

ASD age M

± SD (range) Controls

NVIQ M±SD

(range)

Exp. lang M± SD (range)

Method

Test

Results

correlated with social skills and

sensory profile

Narzisi et al. (2013) S Accuracy 22 (22) 9.8 ± 3.7 (5–

16)

TD WISC-III PIQ 72–

141 (103.4

± 16.3)

NEPSY-II Language scores

similar to controls

P NEPSY-II TD > ASD on production of articulatory

sequences and tongue twisters

Noterdaeme (2002) S/NS Accuracy 11 (8), 2.7:1 9.8 ± 2.3 TD, ELD,

RLD

KABC 103 ± 14 HTLF IS 38 ± 15 P - Per neurological exam, ASD =TD on

oral motor function

Ochi et al. (2019) S Rate 62 (62) 26.9 ± 7 TD WAIS-III FSIQ

106.4 ± 14.3

- I - ASD =TD on speech rate and speech

rate variance

Pang et al. (2016) S/NS Brain activity 21 (17),

4.3:1

11.4 ± 3.2 (6–

17.6)

TD WASI NVIQ 96.4

± 18.1

OWLSII-OE 88.6 ± 24.4 I - During nonspeech task, ASD > TD on

magnitude and delayed latency in

motor control areas and magnitude

in an executive control area. During

phoneme production, ASD > TD on

latency delays in frontal and

temporal language processing areas.

During oromotor sequencing,

ASD > TD on magnitude and

delayed latency in a sensory

integration area

Parish-Morris et al.

(2018)

S Consistency,

diversity

17 (15),

7.5:1

26.9 ± 7.3 TD WASI-II FSIQ

102.1 ± 19.8

- I - TD > ASD on mouth movement diversity

Parmeggiani et al. (2019) F Sucking reflex 105 (82),

3.6:1

1.1 ± 0.8

(0.3–4)

- Various - R - Absent sucking reflex in 16.2% of ASD

sample

Pascolo and Cattarinussi

(2012)

F Muscle activity 7 (7) 7.3 ± 1.8 TD WISC-R > 70 - I - ASD =TD on mylohyoid activity and

timing of initiation of mouth opening

when bringing food to mouth

Patel et al. (2020) S Rate 55 (45),

1.2:1

16.6 ± 6.6

(6.5–

35.1)

TD WISC-4 FSIQ

104.22 ± 12.03

- P/I - TD > ASD on speech rate instrumentally,

but not perceptually

Paul et al. (2008) S Duration 46 (43),

14.3:1

13.2 ± 4.4

(7.3–

28.6)

TD WISC-3 PIQ 95.0

± 20.5

CELF-III EL 99.7 ± 21.5 I T-TRIP No differences in syllable duration among

uncombined ASD groups (HFA, AS,

PDD-NOS). For the combined ASD

group, ASD > TD on difference

between duration of stressed and

unstressed syllables

Peter et al. (2019) S Accuracy 2 (1), 1:1 6.7 ± 3 (4.6–

8.8)

TD RIAS-NII (84) “Severely delayed”P GFTA-2 Two siblings with dx of ASD and CAS

demonstrated reduced articulatory

accuracy, vowel errors, inconsistent

errors, and reduced DDK rates.

(Continues)

MAFFEI ET AL.19

TABLE 3 (Continued)

Author(s) Domain

Features

nASD (M),

M:F ratio

ASD age M

± SD (range) Controls

NVIQ M±SD

(range)

Exp. lang M± SD (range)

Method

Test

Results

Shared genetic variants contributing

to ASD and CAS were proposed

Peterson et al. (2016) F Feeding difficulty 6 (6) (4–6) - - - P - Reduced ability to clear mouth of food

within 30 s for most of ASD sample

Peterson et al. (2019) F Feeding difficulty 6 (6) (3–5) - - - P - Reduced ability to clear mouth of food

within 30 s

Petinou (2021) S Accuracy,

inventory,

intelligibility

1 (1) 4 - - - P - Reduced phonetic inventory, percent

consonants correct, words correct,

and intelligibility

Plumb and Wetherby

(2013)

S Accuracy 50 (43),

6.1:1

21.3 ± 1.9

(18–26.9)

TD, DD MSEL NVDQ 76.0

± 25.8

- P - TD > ASD on proportion of

vocalizations containing at least a

vowel. ASD > TD on proportion of

atypical and distress vocalizations.

Within ASD group, vocalizations

containing a vowel were correlated

with developmental levels, and

communicative vocalizations

uniquely predicted age 3 EL

Rainsdon (2018) S Accuracy 3 (2), 2:1 4 ± 0.2 (3.8–

4.2)

- - SPELT-P2 54–69 P GFTA-3 All participants in the below-average

range on the GFTA

Rogers et al. (1996) NS Accuracy 17 (15),

7.5:1

2.9 ± 0.3

(2.2–3.4)

LrnD, ID,

RLD,

GenD

WISC-R FSIQ 89.4

± 12.1

- P - Controls > ASD on non-meaningful

sequential facial imitations.

ASD =Controls on non-meaningful

single facial imitations and single or

sequential meaningful facial

imitations

Rogers et al. (2003) NS Accuracy 24 (20), 5:1 15.5 ± 3.1

(11–23)

TD, DD,

GenD

MSEL NVMA 12–

44 (23.7 ± 6.3)

- P - TD > ASD, DD > ASD on oral imitation

task. In ASD group, oral-facial

imitation moderately correlated with

ASD severity and joint attention, but

not with EL

Samad et al. (2019) NS Magnitude 10 (10) 13.5 ± 2.4 TD Incl. criteria >70 - I - TD > ASD on activation magnitudes of

FAUs and correlations between

FAUs, although ASD > TD on

activation of mouth frown

Schoen et al. (2011) S Accuracy,

inventory,

vocalization

quality

30 (23),

3.3:1

2.4 ± 0.4

(1.5–3.0)

TD - VABS EL AE 1.2 ± 0.4 P - ASD > TD on number of atypical

nonspeech vocalizations.

TDA > ASD =TDL on number of

early, middle, late, and total

consonants. TDA > ASD on English

consonant blends. ASD > TDA on

number of atypical blends

(Continues)

20 MAFFEI ET AL.

TABLE 3 (Continued)

Author(s) Domain

Features

nASD (M),

M:F ratio

ASD age M

± SD (range) Controls

NVIQ M±SD

(range)

Exp. lang M± SD (range)

Method

Test

Results

Sheinkopf et al. (2000) S Duration,

vocalization

quality

15 (13),

6.5:1

3.7 ± 0.7 DD MPSMT MA 22.13

± 5.07

RDLS EL AE 1.2 ± 0.2 P - ASD =TD on proportion of syllables

containing vowel sounds and

proportion of syllables containing

abnormally long vowels

Shriberg et al. (2001) S Accuracy, rate,

intelligibility

30 (30) 5.8 ± 1.2 (4–

7.9)

- WISC-3 PIQ (89.0

± 23.8)

HFA TLC:E (8.2 ± 4.0); AS

TLC:E (8.3 ± 4.0)

P - HFA > TD and AS > TD on residual

distortion errors. HFA > TD on

“slow articulation/pause time”and

“slow/pause time”ratings.

HFA > AS on “slow articulation/

pause time”ratings

Shriberg et al. (2011) S Accuracy, rate,

consistency,

duration,

resonance,

pausing

46 (36),

3.6:1

6 ± 1.2 TD, CAS,

SSD

WISC-4 PIQ 67–149

(102.8 ± 16.1)

- P/I - Results suggest a modest increase in risk

of Speech Delay, substantial increase

in risk of Speech Error, and no

elevated risk for CAS in verbal ASD.

55% of ASD sample had

lengthened vowels, 55% had

increased percentage of phoneme

distortion, and 25% had slow

speaking rate

Shriberg et al. (2019) S Accuracy,

duration,

formants,

pausing

42 (33),

3.7:1

21.2 ± 10.7

(10–49)

GenD, TBI KBIT-2 (104.3

± 15.7)

- P/I - 83.3% of ASD group classified as normal

speech acquisition, 16.7% with

Speech Delay or Persistent Speech

Delay, 0% with Speech Errors or

Persistent Speech Errors. 85.7% of

ASD group had no motor speech

disorder, 14.3% had speech motor

delay, 0% had childhood dysarthria

or CAS

Spek et al. (2020) F Feeding difficulty 89 (53),

1.5:1

38.5 ± 12 TD - - Q SWEAA ASD =TD on motor control for eating;

within ASD group men had more

motor control problems than women

Stevenson et al. (2017) NS Accuracy 13 (9), 2.3:1 8 ± 4.1 (3–

18)

TD - SCQ communication 6.9 ± 2.4 Q - TD > ASD on oral motor skills. Oral

motor skills were negatively

correlated with autistic traits and

positively associated with pragmatic

language skills

Sullivan et al. (2013) S Accuracy,

formants,

rhythm

39 (29),

2.9:1

1 ± 0.3 (1.5–

2.5)

TD, DD - MSEL EL 26.9 ± 9.2 (20–56) I - Some of ASD group ≠TD on a measure

associated with place of articulation.

Articulatory features were

significantly associated with RL in

ASD group

Talkar et al. (2020) S 5 (5) 7.2 ± 0.4 TD FSIQ 105–135 (124) - I -

(Continues)

MAFFEI ET AL.21

TABLE 3 (Continued)

Author(s) Domain

Features

nASD (M),

M:F ratio

ASD age M

± SD (range) Controls

NVIQ M±SD

(range)

Exp. lang M± SD (range)

Method

Test

Results

Formants,

coordination

ASD > TD in the variance of F2 during

syllable sequencing and free speech

and in the variance of F3 during

sustained vowels. Correlation

between F0 and formant values

during connected speech perfectly

discriminated ASD versus TD

Thurm et al. (2007) S/NS Accuracy 83 (71),

5.9:1

(2–5) DD Various Age 5 VABS composite EL AE

ratio 0.4 ± 0.3

Q SICD, VABS-

TD > ASD on parent reports of child

imitating sounds of adults

immediately after hearing them.

Imitation of adult sounds

discriminated groups which did and

did not acquire EL at age 5

Tierney et al. (2015) S/NS CAS dx 11, 3.3:1 (2–4.6) - - - P KSPT 63.6% of ASD group met criteria for CAS

Trembath et al. (2019) S Vocalization

quality

23 (17),

2.8:1

4.1 ± 0.8

(2.7–5.6)

- MSEL-DQ 64.1

± 21.9

VABS EL 20.5 ± 11.8 (3–47);

MSEL EL 22.9 ± 11.1 (7–

46)

P - Change in vocalization ratio over time

correlated with EL and nonverbal

cognition; vocalizations per minute

not correlated with language skills

van Dijk et al. (2021) F Feeding difficulty 80 (55),

2.2:1

4 ± 1.1 (0–0) TD WPPSI/SON-R/

BSID 90.32

± 18.25

- Q MCH-FS ASD =TD on caregiver reports of

chewing problems

Van Santen et al. (2010) S Duration 26 6.6 ± 1.3 (4–

TD WPPSI-3 PIQ/PRI

(117.63 ± 11.48)

- I PEPS-C ASD =TD on duration of syllables

during lexical stress, emphatic stress,

or contrastive stress tasks

Vashdi et al. (2020) S CAS dx 170 - - - R - 61% of children with CAS or sCAS also

had a dx of ASD

Velleman et al. (2010) S/NS Accuracy, rate,

consistency,

duration,

formants,

pausing

10 (9), 9:1 5.4 ± 0.9

(4.2–6.3)

TD, sCAS NVIQ 70–90 PLS-4-EC (70.8 ± 15.6) P/I/Q VMPAC Parents of 60% of ASD group reported

signs of CD, CAS, or both. 6/10 in

ASD group exhibited severe focal

oromotor control deficits (one

moderate), 4/10 exhibited severe

speech motor deficits (four

moderate). ASD > TD and

ASD > sCAS on multiple formant

values. ASD > TD on duration of

some vowels. TD > ASD on average

variation in speech duration

Vissoker et al. (2019) F Feeding difficulty 105 (105) 3.4 ± 4.3 (2–

TD - - Q AEQ ASD > TD on caregiver reports of

chewing and swallowing problems

Whitehouse et al. (2008) S/NS Accuracy 34 (33),

33:1

10.8 ± 2.8

(7.2–

15.8)

DLD WASI 80–137

(105.0 ± 14.0)

ERRNI MLUw 89.8 ± 13.4 P NEPSY ASD with language impairment > ASD

with appropriate language on

oromotor sequencing. ASD with

(Continues)

22 MAFFEI ET AL.

TABLE 3 (Continued)

Author(s) Domain

Features

nASD (M),

M:F ratio

ASD age M

± SD (range) Controls

NVIQ M±SD

(range)

Exp. lang M± SD (range)

Method

Test

Results

language impairment > SLI on

oromotor sequencing. Both ASD

groups > SLI on sentence repetition.

ASD with appropriate NWR

skills =ASD without appropriate

NWR skills on oromotor sequencing

Wynn et al. (2018) S Rate 30 (21),

2.3:1

19.2 ± 10.4

(6–40)

TD Adults >90 Child group CELF-V EL 88.7 I - TD adults entrained speech rate; ASD

adults, ASD children, and TD

children did not

Wynn et al. (2022) S Accuracy 30 (21),

2.3:1

19.2 ± 10.4

(6–40)

TD Adults >90 Adequate to participate in task I - TD > ASD on articulatory precision

Yan et al. (2021) S Accuracy 30 (26),

6.5:1

5.7 ± 1.3 TD PTONI 59.53

± 12.32

- P - Reduced phonemes correct at baseline

Yoder et al. (2015) NS/F Accuracy,

inventory,

feeding

difficulty

87 (71),

4.4:1

2.9 ± 0.6

(1.7–3.9)

- MSEL MA 1:0 ± 0:5 MCDI-WS 3.7 ± 5.0 (0–18) P OME, CSBS Imitative and non-imitative oromotor

skills did not have added predictive

value for EL or RL among initially

NV children with ASD

Zarokanellou et al.

(2022)

S Accuracy 46 (35),

3.2:1

9.1 ± 1.5 (7–

12)

TD RCPM 104.8 ± 14.1 EOWPVT-R raw score 63.3

± 14.2

P TPPD TD > ASD on nonword repetition,

TD > ASD on speech accuracy

Note: Greater than (>) and less than (<) indicate significant difference in performance.

F, feeding; NS, nonspeech; S, speech.

CAS, childhood apraxia of speech; DD, developmental delay; DLD, Developmental Language Disorder; ELD, expressive language disorder; GenD, genetic disorder; HR, high-risk siblings; HR, high-risk siblings without ASD; HR+, high-risk siblings with

ASD; ID, intellectual disability; LrnD, learning disability; MSD, motor speech disorder; RLD, receptive language disorder; sCAS, suspected childhood apraxia of speech; SSD, speech sound disorder; TBI, traumatic brain injury; TD, typically developing.

AE, age equivalent; BSID, Bayley Scales of Infant and Toddler Development; DAS, Differential Ability Scales; DR, developmental ratio; FSIQ, Full-Scale Intelligence Quotient; KABC, Kaufman Assessment Battery for Children; KBIT, Kaufman Brief

Intelligence Test; LIPS, Leiter International Performance Scale; MA, mental age; MPSMT, Merrill-Palmer Scale of Mental Tests; MSEL, Mullen Scales of Early Learning; NV, nonverbal; NVDQ, Nonverbal Developmental Quotient; NVIQ, nonverbal IQ;

NVMA, Nonverbal Mental Age; PIQ, Performance IQ; PRI, Perceptual Reasoning Index; PTONI, Primary Test of Nonverbal Intelligence; RCPM, The Raven’s Colored Progressive Matrices; RIAS, Reynolds Intellectual Assessment Scales; RPM, The

Raven’s Progressive Matrices; SON-R, Snijders-Oomen nonverbal intelligence tests; TONI, Test of Nonverbal Intelligence; VR, Visual Reception; WAIS, Wechsler Adult Intelligence Scale; WASI, Wechsler Abbreviated Scale of Intelligence; WISC, Wechsler

Intelligence Scale for Children; WPPSI, Wechsler Preschool and Primary Scale of Intelligence.

ADOS, Autism Diagnostic Observation Schedule; AE, Age equivalent; AS, Asperger syndrome; CELF, Clinical Evaluation of Language Fundamentals; DV, Definitional Vocabulary; EC, Expressive Communication; EL, Expressive Language; EOWPVT,

Expressive One Word Picture Vocabulary Test; ERRNI, Expression, Reception and Recall of Narrative Instrument; EV, expressive vocabulary; EVT, Expressive Vocabulary Test; HFA, high-functioning autism; HTLF, Heidelberg Test of Language

Development; IS, Imitation of Grammatical Structures; LUL, longest utterance length; MCDI, MacArthur-Bates Communicative Development Inventory; MLU, mean length of utterance; MLUw, Mean number of words per utterance; MSEL, Mullen Scales

of Early Learning; NDW, Number of different words; NV, Nonverbal; OE, Oral Expression; OWLS, Oral and Written Language Scales; PLS, Preschool Language Scale; RDLS, Reynell Developmental Language Scales; SCQ, Social Communication

Questionnaire; SPELT-P, Structured Photographic Expressive Language Test-Preschool; TLC, Test of Language Competence; TOPEL, Test of Preschool Early Literacy; V, Verbal; VABS, Vineland Adaptive Behavior Scales; WS, Words said.

I, instrumental; P, Perceptual; Q, questionnaire/report; R, medical record review.

See Table 2for test names.

AS, Asperger syndrome; ASD, autism spectrum disorder; CD, childhood dysarthria; DDK, diadochokinetic; dx, diagnosis; EL, expressive language; FAU, facial action unit; FM, fine motor; GM, gross motor; HFA, high-functioning autism; NWR, nonword

repetition; PDD-NOS, Pervasive Developmental Disorder-Not Otherwise Specified; RL, receptive language; SLI, specific language impairment; TDA, typically developing age-matched controls; TDL, typically developing language-matched controls.

MAFFEI ET AL.23

Consistency/stability

Thirteen studies (12%) examined the consistency or sta-

bility of oromotor movements (six perceptual and five

instrumental studies). Eleven of these 13 studies (85%)

found significant abnormalities related to consistency or

stability in a sample of autistic individuals. Interestingly,

of these 11 studies, six reported reduced consistency/

stability (Chenausky et al., 2019,2020,2021;

Mahler, 2012; Shriberg et al., 2011; Talkar et al., 2020)

and five reported increased consistency/stability

(Gladfelter & Goffman, 2018; Kissine et al., 2021;

Kissine & Geelhand, 2019; Parish-Morris et al., 2018;

Velleman et al., 2010) in an ASD group. Findings of

reduced consistency/stability include (1) higher ratings of

“inconsistent errors,”“variable errors,”or “less stable

whole word errors”(Chenausky et al., 2019,2020,2021;

Shriberg et al., 2011); (2) reduced consistency of phoneme

accuracy during DDK repetitions (Mahler, 2012); and

(3) increased variability in formant values during DDK

repetitions, free speech, and sustained vowels (Talkar

et al., 2020). Findings of higher consistency/stability

include (1) greater improvement in motor stability during

a nonword learning task compared to controls

(Gladfelter & Goffman, 2018); (2) greater articulatory

stability in the production of native vowels compared to

non-autistic controls (Kissine et al., 2021; Kissine &

Geelhand, 2019); (3) less average variation in speech

durations (Velleman et al., 2010); and (4) a restricted rep-

ertoire of mouth movements during conversational

speech (Parish-Morris et al., 2018).

Rate

Of the 13 studies (12%) that examined speech or DDK

rate (i.e., the number of syllables or words per unit of

time) among autistic individuals (10 perceptual and six

acoustic studies, including two studies using both

approaches), eight (62%) reported a significant abnor-

mality in a group of autistic individuals and five (38%)

did not report significant group differences. Among the

eight studies that reported a rate abnormality in the ASD

group, six reported a slower rate among autistic speakers

(Chenausky et al., 2019,2020,2021; Patel et al., 2020;

Shriberg et al., 2001,2011), one reported general abnor-

mality of rate without indicating a direction

(Mandelbaum et al., 2006), and one reported that autistic

adults do not entrain their speaking rate (i.e., modify rate

to match a communication partner’s rate) while non-

autistic adults do (Wynn et al., 2018).

Duration

Of the 11 studies (10%) that examined the duration of

phonemes or syllables among autistic individuals

(10 instrumental studies and one perceptual study), nine

(82%) reported a significant difference between autistic

and non-autistic individuals and two (18%) reported simi-

lar rates between autistic individuals and controls. Of the

eight studies that reported a significant abnormality, four

reported longer sound durations in the ASD group

(Diehl & Paul, 2013; Grossman et al., 2010; Kissine &

Geelhand, 2019; Shriberg et al., 2011; Velleman

et al., 2010), one reported a group difference without

indicating the direction of the finding (Lyakso

et al., 2017), and three studies reported significantly less

difference in duration between speaking conditions by

autistic speakers (i.e., between emotional states [Hub-

bard & Trauner, 2007] and between stressed and

unstressed syllables [Lyakso et al., 2016; Paul

et al., 2008]).

Vowel production/formant values

Eight studies (7%) used acoustic methods to examine the

formant values of phonemes (i.e., resonant frequencies

corresponding to vocal tract configurations) produced by

autistic speakers. Among these studies, six (75%)

reported significant group differences between autistic

and control groups (Kissine et al., 2021; Kissine &

Geelhand, 2019; Lyakso et al., 2016,2017; Talkar

et al., 2020; Velleman et al., 2010), one study examined

two autistic children intended to represent subgroups of

ASD but without the use of a control group (Chenausky

et al., 2021), and one study found no significant differ-

ence between groups (Sullivan et al., 2013). Abnormal

findings among these studies include (1) reduced F1–F3

dispersion among autistic speakers, suggesting more

invariant articulatory gestures during vowel production

(Kissine et al., 2021; Kissine & Geelhand, 2019); (2) an

increased attraction index when producing non-native

vowels, suggesting that autistic speakers produce vowels

with formant values closer to those of one of their native

vowels than to target non-native vowels compared to

controls (Kissine et al., 2021); (3) larger formant trian-

gles, suggesting a larger magnitude of tongue movements

during vowel production in autistic speakers (Lyakso

et al., 2016,2017; although note that statistical signifi-

cance was not reported); (4) increased variance of for-

mants during various speech tasks (Talkar et al., 2020);

and (5) higher formant values among autistic speakers

during prolonged vowels (Velleman et al., 2010) and dur-

ing emotional speech (Lyakso et al., 2016). Chenausky

et al. (2021) examined the speech of two minimally verbal

autistic children hypothesized to belong to different sub-

phenotypes (i.e., motor speech disorder and motor speech

disorder +auditory processing disorder). Using formant

analyses, they found a similar level of phoneme distortion

between the two children, but that the child with only a

suspected motor speech disorder had a larger searching

articulation index, larger token-to-token variability, and

a larger vowel space.

Coordination/coarticulation

Six studies (6%) addressed oromotor coordination among

autistic individuals (four perceptual and one instrumental

study). Each found significant differences indicating

decreased oromotor coordination among autistic

24 MAFFEI ET AL.

individuals. Findings include (1) “difficulty with coarticu-

lation”or “difficulty with initial configurations or transi-

tions”in autistic children or subgroups of autistic

children (Chenausky et al., 2019,2020,2021), (2) lower

performance on a test of oromotor sequences for high-

functioning autistic children compared to non-autistic

controls (Narzisi et al., 2013), (3) decreased temporal

coordination in autistic individuals during a speech task

using a metronome (Franich et al., 2020), and (4) a lower

complexity in the correlation of formants and facial

movements during a reading task, suggesting a decreased

independence of movements compared to control sub-

jects (Talkar et al., 2020).

Vocalization quality

Five studies (5%) examined vocalization quality among

young autistic children (i.e., whether vocalizations were

considered speechlike, nonspeechlike, or otherwise

abnormal). Three studies reported significant differences

between autistic children and comparison groups includ-

ing (1) significantly fewer speechlike vocalizations among

younger siblings of autistic children who later received an

ASD diagnosis compared to younger siblings who did

not receive an ASD diagnosis and typically developing

controls (Chenausky, Nelson, & Tager-Flusberg, 2017),

(2) fewer vocalizations containing a vowel and more

vocalizations not containing a vowel in autistic children

than typically developing peers (Plumb &

Wetherby, 2013), and (3) significantly more atypical

vocalizations among autistic children, with high-pitched

squeals accounting for much of this difference (Schoen

et al., 2011).

Intelligibility

Four studies (4%) examined the intelligibility (i.e., the

amount of speech that is understood by a listener) of

autistic individuals, all using perceptual methods. Three

of these studies (75%) reported low intelligibility among

autistic speakers (Koegel et al., 1998; Lyakso et al., 2017;

Petinou, 2021), although it should be noted that Petinou

(2021) was a case study of one child and Lyakso et al.

(2017) did not report the statistical significance of the

reduced intelligibility in autistic children compared to

their control group.

Nonspeech oromotor skills

Twenty-eight studies (26% of all studies in this review)

investigated the nonspeech oromotor skills of autistic

individuals, and 25 studies reported whether the skills

were normal or abnormal. The remaining three studies

provided information regarding performance among sub-

groups of autistic individuals or regarding correlations

between nonspeech oral motor skills and language out-

comes rather than providing a comparison with norms or

a control group. Twenty studies (80% of those reporting

an outcome) reported abnormalities among a sample of

autistic individuals and five studies (Dalton et al., 2017;

Deshmukh, 2012; Espanola Aguirre & Gutierrez, 2019;

Mahler, 2012; Noterdaeme, 2002) reported no significant

difference in nonspeech oral motor skills between and

ASD group and a control group.

Among the studies reporting a significant difference

in nonspeech oromotor skills, a commonly reported find-

ing was that autistic individuals scored in the impaired

range or significantly lower than a control group on a test

of oromotor imitation such as the KSPT (Adams, 1998;

Chenausky et al., 2019; Chenausky, Norton, &

Schlaug, 2017; Gernsbacher et al., 2008; Kim &

Seung, 2015; Tierney et al., 2015), VMPAC (Biller &

Johnson, 2019,2020; Dalton et al., 2017; Velleman

et al., 2010), POME (Amato & Slavin, 1998; McDaniel

et al., 2018; Yoder et al., 2015), Com DEALL Oro

Motor Assessment (Belmonte et al., 2013), SIPT

(Bodison, 2015), or OSMSE (Deshmukh, 2012). These

tests assess an individual’s ability to perform nonspeech

oral movements such as opening the jaw; elevating and

lateralizing the tongue; spreading and puckering lips; per-

forming sequences of oromotor tasks; and performing

functional behaviors like sucking and biting.

Other findings include differences in movements of

the lips during emotional expression (Manfredonia

et al., 2019; Samad et al., 2019) and differences in brain

activity during nonspeech oromotor tasks (Pang

et al., 2016) including increased magnitude and delayed

latency in motor control areas as well as increased magni-

tude in an executive control area. Of note, Belmonte

et al. (2013) found that nonspeech oromotor skills varied

independently of gross and fine motor skills among autis-

tic children, offering additional motivation for studying

these skills in addition to speech production abilities.

Feeding skills

Nineteen studies (18%) examined feeding-related oromo-

tor skills among autistic individuals—10 using caregiver

questionnaires, six using perceptual methods, two using

instrumental methods, and one using medical record

review. Of these studies, 17 reported whether motor feed-

ing skills were normal or abnormal, and 11 of those

17 (65%) reported feeding-related oromotor deficits

(Amato & Slavin, 1998; Brisson et al., 2012; Cattaneo

et al., 2007; Demartini et al., 2021; Gal et al., 2022;

Leader et al., 2020; McDaniel et al., 2018; Nakaoka

et al., 2020; Parmeggiani et al., 2019; Peterson

et al., 2016; Vissoker et al., 2019).

Abnormal findings include decreased scores on

motor-related portions of the OME (“eating behaviors”;

Amato & Slavin, 1998; McDaniel et al., 2018), SWEAA

(“motor control”; Demartini et al., 2021), AEQ (“chew-

ing and swallowing problems”; Gal et al., 2022; Vissoker

et al., 2019), STEP-CHILD (“chewing problems”; Leader

et al., 2020), and ASD-MBQ (“oral motor function”;

Nakaoka et al., 2022). Brisson et al. (2012) reported

MAFFEI ET AL.25

significantly less anticipatory mouth opening in response

to an approaching spoon among autistic infants com-

pared to typically developing infants, a finding which

may be considered alongside Cattaneo et al. (2007), who

reported significantly less mylohyoid muscle activity

among autistic children both when observing someone

reach for, grasp, and eat food and also when performing

the same action themselves. Parmeggiani et al. (2019)

used a medical record review of 105 autistic individuals

to reveal that 16.2% of the sample had an absent sucking

reflex as a newborn. Peterson et al. (2016) observed six

autistic children (aged 4–6 years) for “mouth clean”

(i.e., the absence of food larger than a grain of rice in the

child’s mouth 30 s after a bite entered the mouth) and

noted that four of the children (67%) had a mean “mouth

clean”of 0% over five attempts.

Associations between oromotor performance

and other skills in ASD

Twenty-six studies (24%) reported information regarding

associations or correlations between oromotor skills and

a variety of linguistic, cognitive, behavioral, or demo-

graphic variables among autistic individuals.

Expressive language

The relationship between oromotor skills and expressive

language skills was investigated in 12 studies, of which

nine (75%) reported an association between abnormal

oromotor skills and decreased expressive language abili-

ties. Specifically, it has been reported that verbal autistic

children (i.e., those who produce and vocalize consonant-

vowel syllables and use them during communicative

attempts) significantly outperform nonverbal autistic

children on measures of eating behaviors, voluntary non-

verbal oral skills, and pre-speech/speech behaviors

(Amato & Slavin, 1998). Oromotor skills have been

shown to accurately differentiate highly, moderately, and

minimally fluent autistic children (Gernsbacher

et al., 2008), and speech production ability accounts for

significant levels of variance in the number of different

words produced by minimally verbal autistic individuals

(Chenausky et al., 2019). Additionally, oromotor skills

have been found to positively correlate with both pre-

intervention expressive language and with learning rates

during expressive language intervention (Belmonte

et al., 2013) and with a caregiver questionnaire assessing

communication skills (Nakaoka et al., 2020). Whitehouse

et al. (2008) found that autistic children with age-

appropriate language outperformed autistic children with

language impairment on an assessment of oromotor

sequencing. Thurm et al. (2007) reported that perfor-

mance on the item “imitating sounds of adults immedi-

ately after hearing them”from the VABS at age 2 was a

significant discriminator of autistic children with and

without expressive language deficits at age 5. Trembath

et al. (2019) found that children with higher expressive

language scores (per the MSEL and VABS) demon-

strated greater increases in the vocalization ratio over

time. Finally, Sullivan et al. (2013) showed that the num-

ber of vocalizations produced by autistic children is

related to the variability of acoustic features correspond-

ing to the placement of articulators during speech.

Receptive language

Six studies examined correlations between oromotor

skills and receptive language. Five of these studies (83%)

reported a significant relationship. As with expressive

language, Belmonte et al. (2013) found that oromotor

skills are positively correlated with pre-intervention lan-

guage abilities as well as receptive language learning rates

during intervention. Receptive vocabulary differentiates

minimally and low verbal autistic children whose speech

production is within normal limits from those with sus-

pected CAS, those with non-CAS speech impairment,

and those with insufficient speech for perceptual rating

(Chenausky et al., 2019). Kjelgaard and Tager-Flusberg

(2001) found that word-level speech production ability

measured via the GFTA was associated with receptive

vocabulary. Thurm et al. (2007) reported that perfor-

mance on the item “imitating sounds of adults immedi-

ately after hearing them”from the VABS at age 2 was a

significant discriminator of autistic children with and

without receptive language deficits at age 5. Finally, Sul-

livan et al. (2013) found that acoustic measures of place-

ment of the articulators were associated with receptive

language skills among autistic children.

Expressive-receptive disparity

The relationship between receptive and expressive lan-

guage in ASD is of theoretical interest because of its

potential to reveal whether deficits of communicative

speech are due to underlying deficits in the use of lan-

guage or whether they can be attributed to expressive

language-specific abilities such as oromotor skills. Of the

two studies directly investigating this topic, Belmonte

et al. (2013) found that a receptive-expressive language

disparity (i.e., receptive language [RL] superior to expres-

sive language [EL]) was associated with oromotor impair-

ments among a subgroup of autistic children, while

McDaniel et al. (2018) found that although attention

toward a speaker was positively correlated with a

receptive-expressive vocabulary discrepancy (EL > RL),

oromotor performance was not correlated with a hypoth-

esized receptive-expressive vocabulary discrepancy in the

opposite direction (RL > EL).

Social skills

The relationship between oromotor skills and social func-

tioning was explored in three studies, each of which

reported significant correlations with measures of social

skills. Nakaoka et al. (2022) found that oromotor func-

tion was moderately correlated with scores from the

26 MAFFEI ET AL.

Social Communication Questionnaire (SCQ; Rutter, Bai-

ley, & Lord, 2003). Plumb and Wetherby (2013) found

moderate correlations between both total vocalizations

and transcribable vocalizations (i.e., syllabic vocaliza-

tions containing at least a vowel which may also contain

a consonant) with scores from the social composite score

of the CSBS (Wetherby & Prizant, 2002), and Trembath

et al. (2019) found that the ratio of speech to nonspeech

vocalizations in autistic children was significantly associ-

ated with scores on the SCQ.

Other skills

Studies included in this review have explored associations

between oromotor skills and other linguistic, cognitive,

behavioral, or demographic variables. In each case, sig-

nificant associations were reported. Positive associations

of various strengths have been found between oromotor

abilities and fine motor skills (Belmonte et al., 2013),

manual motor skills (Gernsbacher et al., 2008), joint

attention (Dalton et al., 2017), nonword repetition skills

(Zarokanellou et al., 2022), naming skills (Zarokanellou

et al., 2022), developmental outcomes as measured by the

Mullen Scales of Early Learning (MSEL; Mullen, 1995)

(Plumb & Wetherby, 2013), symbolic behaviors

(Plumb & Wetherby, 2013), pragmatic language

(Stevenson et al., 2017), nonverbal IQ (Chenausky

et al., 2019), sensory profile (Nakaoka et al., 2022), and

daily living skills (Nakaoka et al., 2022). Negative associ-

ations have been reported between oromotor abilities and

autism characteristics (Stevenson et al., 2017) and inter-

nalizing conditions (Lundin Remnélius et al., 2022).

DISCUSSION

Our results indicate that despite the widespread notion

that speech production is relatively spared—at least when

assessed perceptually—among autistic individuals

(Kjelgaard & Tager-Flusberg, 2001; Rapin &

Dunn, 2003), the majority of available evidence indicates

abnormal oromotor control in this population. The per-

centage of studies reporting oromotor impairments var-

ied by domain (i.e., speech, nonspeech, and feeding) and

behavior analyzed. However, the majority of studies,

whether conducted with perceptual or instrumental

methods, indicated that autistic individuals demonstrate

significant abnormalities in oromotor functioning (85%

and 81%, respectively). For example, 85% of studies

examining speech accuracy and 80% of studies examining

nonspeech oral movements among autistic individuals

reported deficits. For some less commonly studied fea-

tures such as coarticulation/coordination, 100% of studies

reported an abnormality.

It is critical to note the numerous methodological

issues, sampling challenges, and contradictory findings

present in the included studies. These factors (discussed

in further detail below), along with the relatively small

number of studies analyzing particular oromotor behav-

iors, lead us to conclude that the current level of under-

standing of oromotor functioning in ASD is poor and

that additional rigorous research is necessary before we

can draw definitive conclusions. Below we will first dis-

cuss the findings of each of our research questions and

then explore several limitations associated with the body

of literature presented above.

What methods have been used to investigate

oromotor functioning among individuals

with ASD?

Sixty-one percent of the studies in this review used per-

ceptual (i.e., auditory and/or visual) methods. Perceptual

analyses often serve as the gold standard of assessment

for a variety of speech features and are used extensively

to diagnose the type and severity of motor speech disor-

ders, inform treatment targets, and monitor progress over

time (Duffy, 2019). However, the over-representation of

perceptual assessments in this review is likely one factor

influencing the mixed results across studies. Perceptual

assessments typically require intensive clinical training,

are vulnerable to rater bias (Borrie et al., 2012; Che-

nausky, Maffei, et al., 2022; Kent, 1996), and may be too

coarse and unreliable for detecting subtle oromotor defi-

cits (Green, 2015). Even experienced listeners are suscep-

tible to top-down linguistic processes during speech

perception such as disregarding acoustic errors to com-

prehend a speech signal more effectively (Bond &

Garnes, 1980). Indeed, the differences among perceptual

judgments made by expert judges are often larger than

the differences needed for diagnostic classification

(Kent, 1996). To take one example from this review,

nearly all of the assessments of nonspeech oromotor

functioning included in this study were obtained via audi-

tory and visual perceptual methods using tools such as

the OSMSE or VMPAC. The reliability of such measures

is contingent on the assumption that all raters have simi-

lar internal standards of parameters such as typical

ranges of motion during lip retraction or tongue laterali-

zation. Training materials and procedures are often pro-

vided with these assessment tools to improve reliability;

however, other sources of bias including listener familiar-

ity with a speaker (Tjaden & Liss, 1995) are known to

significantly impact perceptual judgments. Other studies

in this review made judgments regarding nonspeech oro-

motor skills based on novel tasks, modified versions of

existing tools, or prototypes of tools still in development,

reducing the reliability of these judgments.

Instrumental assessment methods including acoustic

analysis, EMG, MEG, MRI, ultrasound imaging, and

facial motion tracking were used in only 36 studies

(34%). These methods offer objective data, which miti-

gate the perceptual biases discussed above. Acoustic

methods, which are replicable, non-invasive, and widely

MAFFEI ET AL.27

available, were by far the most commonly used instru-

mental approach in this review. Some studies included

both perceptual and instrumental assessment and draw

attention to the differences between these approaches.

For example, Patel et al. (2020) found a significant differ-

ence between autistic children and their typically develop-

ing peers in acoustic—but not perceptual—measures of

speaking rate, suggesting that instrumental methods can

detect subtle differences unavailable to even the trained

human ear.

However, the relationship between an acoustic signal

and perceptual speech features is often complex, particu-

larly when measuring gestalt perceptual constructs such

as articulatory or prosodic accuracy. For instance, judg-

ments of articulatory precision may not be correlated

with a single acoustic feature but rather with a combina-

tion of features such as formant transitions and speaking

rate (Chiu et al., 2021). In general, there is a need for fur-

ther research regarding the validity of acoustic measures

of constructs such as articulatory function (Allison

et al., 2020; Rowe et al., 2021).

Kinematic analyses such as facial motion tracking

and ultrasound tongue imaging, used in only six studies

in this review, offer highly precise data and crucial physi-

ological context for differences in motor function,

although require specialized equipment and time-

consuming data analysis. Incidentally, five of the six

studies utilizing kinematic technologies in this review

found significant abnormalities among individuals with

ASD (Gladfelter & Goffman, 2018; Manfredonia

et al., 2019; McKeever et al., 2022; Parish-Morris

et al., 2018; Samad et al., 2019). There is high potential

for kinematic techniques that have been used with other

populations to aid the study of oromotor function in

ASD, such as lip movement speed and duration

(Yunusova et al., 2010), lip shape (Iuzzini-Seigel

et al., 2015), temporal coordination of the articulators

(Green et al., 2000), jaw motion during chewing (Simione

et al., 2016; Wilson et al., 2012), and tongue and jaw

movements during swallowing (Perry et al., 2018).

The relative lack of instrumental investigations, which

are capable of objectively quantifying perceptual anoma-

lies in disordered speech, appears to be a gap in this liter-

ature. It is interesting to note that both perceptual and

instrumental methods resulted in nearly the same percent-

age of studies reporting an oromotor deficit (i.e., 85% of

perceptual studies and 81% of instrumental studies). The

similarity of these two percentages suggests that both

approaches are tapping into real deficits and offer rela-

tive methodological strengths. For instance, perceptual

methods will likely remain the gold standard for assessing

features such as phonetic inventory and vocalization

quality, while instrumental measures become increasingly

common for measures constructs such as rate, duration,

consistency, and certain aspects of accuracy.

Two major trends related to the use of assessment

tools were also revealed in this review. First, assessment

tools designed specifically to assess oromotor functioning

(e.g., the VMPAC or OSMSE) were used in only 11 stud-

ies. The interpretation of results regarding oromotor

skills obtained using tools designed to assess other

domains (e.g., prosody, expressive language, and general

development) is significantly limited by these tools’lack

of sensitivity in measuring oromotor function; in fact,

some of these assessments include as few as one item

related to the complex construct of oromotor function-

ing. Second, norm-referenced standardized assessment

tools were used in 33 total studies. While a control group

is often used instead of normative data, this under-

utilization of norm-referenced assessments underscores a

critical need for the development and use of valid and

reliable assessment tools in this area.

Sample size is a consideration that must be made

carefully when designing a study. Within this review,

39% of studies had a sample size of under 20 individuals

with ASD, and 17% had a sample size of fewer than

10 participants. Small samples, including single case stud-

ies, offer several advantages such as allowing for the col-

lection of a large amount of data that cannot easily be

obtained from a large sample and for initially exploring a

novel technique or hypothesis. However, findings of stud-

ies with small samples are significantly limited in their

statistical power and thus their generalizability to the

wider population of individuals with ASD.

What oromotor behaviors (including speech

skills) have been investigated?

The majority of studies (78%) in this review examined

speech production, compared to 25% of studies examin-

ing nonspeech oromotor control and 18% of studies

examining oromotor control related to feeding. The study

of speech production in this population is critical as

researchers investigate the nature of expressive communi-

cation deficits among autistic individuals. However, the

study of nonspeech oral motor control is imperative. The

reason lies in the uniqueness and domain-specificity of

speech, which involves distinct neural networks specifi-

cally tuned to the motor processes involved in speech pro-

duction. These processes are distinct from those of

nonspeech oral movements in a variety of ways including

relative muscular forces, velocities, durations, rhythmic

scaffoldings, and contextual embeddings (Ziegler &

Ackermann, 2013). Conclusions regarding nonspeech

oromotor skills cannot be made based on research study-

ing speech, and vice versa (Weismer, 2006). Studying

nonspeech and speech skills in parallel will also shed light

on questions related to whether motor impairments in

ASD are present globally throughout the motor system

or are manifested in the context of cognitively demanding

tasks such as language production. Thus, there appears

to be a relative dearth of research regarding nonspeech

oral motor control among autistic individuals. In the

28 MAFFEI ET AL.

context of examining oromotor skills as potential predic-

tors of language impairment, we cannot ignore the possi-

bility that even sub-perceptual differences in nonspeech

oromotor skills may serve as valuable features in predic-

tive models.

This review also allows analysis of which behaviors

have been studied perceptually and which have been

studied instrumentally. Several features have been exam-

ined using both approaches—for instance, speech sound

accuracy has been assessed using binary correct/incorrect

ratings in perceptual studies and via measures such as

formant frequencies or voice onset time measurements in

instrumental studies. On the other hand, some features

were only examined perceptually, often in cases in which

a construct of interest is defined in relation to human lis-

teners (e.g., intelligibility, although note recent work

including Jacks et al., 2019 and Gutz et al., 2022 linking

automatic speech recognition and intelligibility). How-

ever, in the case of other features, a lack of research high-

lights the need for acoustic measures of these features.

For instance, while some work has been done to develop

acoustic measures of speech coordination (e.g., Rowe

et al., 2021; Weismer et al., 2003), currently there is a

need to establish valid and reliable acoustic measures of

such features. On the other hand, sophisticated measures

of oromotor coordination can be found in studies using

facial motion capture technology (Green et al., 2000;

Matsuo & Palmer, 2010; van Lieshout & Neufeld, 2014).

What conclusions can be drawn regarding

oromotor skills in ASD?

A large majority (81%) of studies included in this review

reported significant oromotor abnormalities among autis-

tic individuals. However, there is limited consensus

regarding the presence, type, and severity of deficits even

within specific domains (for instance, only 54% of studies

investigating speech rate found significant differences

among individuals with ASD). Additionally, different

abnormalities have been reported within the same fea-

ture, precluding straightforward synthesis and interpreta-

tion. For instance, among the 13 studies investigating

speech rate, findings for individuals with ASD included

typical rate, abnormally slow rate, an abnormal rate

without indication of direction, and a lack of speech rate

entrainment. Nonetheless, it is compelling to note that

for any given feature—whether related to speech, non-

speech oral behaviors, or feeding—at least 50% of the

studies addressing that feature indicated a significant

abnormality among individuals with ASD.

Several findings from this review can be interpreted in

the context of known characteristics of ASD, including

the presence of restricted, repetitive patterns of behavior,

interests, or activities (American Psychiatric

Association, 2013). Of the five instrumental studies inves-

tigating oromotor stability in this review, four support the

presence of reduced movement variability in the oromotor

system among autistic individuals (Gladfelter &

Goffman, 2018; Kissine et al., 2021; Kissine &

Geelhand, 2019; Parish-Morris et al., 2018), which may

be interpreted as a potential extension of “restricted, repet-

itive patterns of behavior.”Further support for this theme

of “restricted”motor behavior can be found in other stud-

ies included in this review that did not directly investigate

motor stability. Talkar et al. (2020) interpreted their

results regarding the coordination of motor acts as sug-

gesting that individuals with ASD may demonstrate

higher levels of coupling in their movements than typically

developing controls; Velleman et al. (2010) found that

their subjects with ASD had less average variation in their

speech durations compared to TD controls. Interestingly,

the results highlighted above stand in contrast with reports

of increased variability in temporal and spatial aspects of

other motor activities in ASD, including reaching, point-

ing, and saccadic movements (Glazebrook et al., 2006;

Gowen & Miall, 2005; Stanley-Cary et al., 2010).

Atypical or absent anticipatory behaviors are not a

diagnostic marker of ASD but have been observed

among autistic children since the earliest descriptions of

the disorder (Kanner, 1943). These deficits are widely

reported in the literature and include significant difficulty

with anticipation during behaviors such as load-lifting

(Martineau et al., 2004; Schmitz et al., 2003). For

instance, children with ASD demonstrate decreased pre-

dictive muscle activity in their left hand when removing a

weight from it with their right hand (Schmitz

et al., 2003). This and similar results suggest an over-

reliance on feedback (reactive) control and an under-

reliance on feedforward (predictive) control in the motor

systems of individuals with ASD. Several studies in this

review examined anticipatory oromotor behaviors; Bris-

son et al. (2012) reported an anticipation deficit observ-

able at 4–6 months of age among infants later diagnosed

with ASD, and Cattaneo et al. (2007) found decreased

activation of the mylohyoid muscle when autistic children

grasped food and brought it to their mouth. The young

age of the subjects in the study by Brisson et al. (2012)

study suggests the potential of such motor behaviors as a

predictor of outcomes in ASD. Ultimately, a lack of

additional studies confirming and expanding on these

findings prevents a clear understanding of whether such

failures of anticipation are related to the motor system or

to other domains (e.g., cognitive processing speed or the

interpretation of social cues).

The movement abnormalities observed across

domains in individuals with ASD (e.g., reduced variabil-

ity, reduced accuracy, deficits of anticipatory behavior,

and disrupted feedback control) do not appear to neatly

fit into a pre-established category of motor disorder

(e.g., dystonia, ataxia, hyperkinesia, or apraxia). The

existing literature raises the question of whether the

motor differences observed in ASD may constitute a dis-

tinct category of motor disorder. This notion has specific

MAFFEI ET AL.29

implications for the characterization of speech error pat-

terns among autistic individuals, which are variable and

do not conform to existing categories such as pediatric

dysarthria or childhood apraxia of speech in obvious

ways. Shriberg et al. (2010) provide a cover term

(i.e., Motor Speech Disorder-Not Otherwise Specified;

MSD-NOS) for speech, prosody, and voice behaviors

that are consistent with motor speech impairment but do

not correspond to the main classifications of apraxia or

dysarthria. Future research focusing on a holistic picture

of the cognitive, motor, and linguistic skills of individuals

with ASD should consider impairment profiles that fall

outside current diagnostic classification schemes and may

be consistent with well-established characteristics of

autistic individuals such as behavioral rigidity and antici-

patory difficulties.

Methodological limitations

Beyond several trends discussed above, an effective char-

acterization of oromotor functioning among individuals

with ASD is hindered by several methodological con-

straints characterizing the included studies.

First, it must be noted that studies with small sample

sizes are often inadequately powered to detect true group

differences and to produce generalizable results. For

instance, Adams (1998) provided one of the most

straightforward accounts of oromotor functioning in

ASD and is often cited as providing evidence for oromo-

tor and motor speech deficits in the population. How-

ever, the study had a sample of only four children with

ASD and four TD controls. Similarly, Tierney et al.

(2015) reported the compelling finding that 64% of their

sample of children with ASD also had comorbid CAS

based on a sample of only 11 children.

A second methodological limitation is the common

reliance on perceptual judgments, which were utilized in

nearly two-thirds of the studies included in this review.

These methods are often considered the gold standard in

speech assessment because of their ecological validity

but, as discussed above, have known limitations includ-

ing multiple sources of variability associated with

auditory-perceptual assessment (Kent, 1996) and the use

of rating schemes that may not be granular enough to

detect subtle oromotor differences (Green, 2015). Strate-

gies for attenuating the limitations of perceptual assess-

ment have been proposed, including the use of carefully

designed reference samples and increased listener training

(Kent, 1996).

Third, many standardized tests of children’s nonver-

bal oral and speech motor performance are limited by

inadequate psychometric development (McCauley &

Strand, 2008). Broome et al. (2017) conducted a system-

atic review specifically examining speech assessments for

children with ASD and noted similar psychometric limi-

tations, highlighting the need for standard procedures to

aid in cross-study comparison. For some less frequently

investigated motor speech features (e.g., consistency,

coordination), standardized tests do not currently exist,

and these skills are defined and examined in a variety of

ways across studies, precluding generalization.

Fourth, assessment tools for examining behaviors like

articulation and nonspeech imitation often utilize dichot-

omous correct/incorrect item scores, which do not make

a distinction between errors due to common developmen-

tal phonological processes or atypical errors representing

disordered development. This provides an obstacle to

understanding the nature of speech sound errors in this

population, including whether they represent atypical or

simply delayed developmental trajectories. Relatedly,

specific error types are often not reported, making char-

acterization of articulatory deficits difficult. For instance,

Tierney et al. (2015) provided results suggesting a high

prevalence of CAS among autistic children, based on

“the presence of speech characteristics of apraxia”during

administration of the KSPT, but did not discuss these

characteristics or their relative presence among their

sample.

Finally, the approach of using control groups

matched on age, IQ, or mental age in ASD language

research, as was done in many papers included in this

review, deserves careful consideration for reasons out-

lined by Tager-Flusberg (2004); in particular, heterogene-

ity of the ASD population, intellectual disability among

ASD participants, developmental changes with age, and

the possibility of recruitment bias have significant poten-

tial to impact the validity of using matched control sam-

ples in language research in ASD.

Sampling challenges

There are numerous significant challenges associated

with obtaining an adequate and representative sample of

oromotor functioning from children with ASD. First, it is

well established that deficits of imitation ability are pre-

sent among individuals with ASD (see Smith &

Bryson, 1994 for a review), although it remains unclear if

such deficits are indicative of neurological impairments

(e.g., difficulty with action planning), impaired social

skills, or deficits in cognitive functions such as forming

mental representations of events. Many of the standard-

ized tests used in the studies in this review require imita-

tion of oral movements and speech sounds, which creates

a challenge for the assessor who must be willing to utilize

alternative methods to elicit attempts and then interpret

assessments cautiously. Autistic individuals also often

demonstrate deficits in receptive language skills

(Kjelgaard & Tager-Flusberg, 2001), with important

implications on their ability to follow task instructions,

and making many tasks—particularly more complex

tasks such as producing sequences of syllables or

nonwords—impractical for many autistic individuals.

30 MAFFEI ET AL.

Additionally, as many as one-third of children with ASD

can be considered minimally verbal (Tager-Flusberg &

Kasari, 2013), meaning that they fail to acquire spoken

language beyond a limited set of single words or func-

tional phrases. This provides another significant obstacle

to oromotor assessment since these subjects may not pro-

duce enough spoken language to assess or may produce

unintelligible utterances than cannot easily be compared

to target productions. Methods for conducting research

with this population have been reviewed and discussed in

detail by Chenausky, Maffei, et al. (2022).

The methods used to recruit samples of autistic indi-

viduals, often influenced by the study’s research ques-

tions, hypotheses, and available resources, also has a

significant influence on participant characteristics and

thus results. For example, a sample recruited through a

speech, language, and feeding clinic will almost certainly

be biased toward higher levels of oromotor deficits

and/or speech sound errors. On the other hand, a

population-based study recruiting through varied sources

(e.g., support groups, schools) may be more representa-

tive of a range of oromotor strengths and difficulties.

Similarly, the research questions addressed by a given

study will influence its sample. For instance, studies

investigating speech production deficits in minimally ver-

bal autistic children (e.g., Chenausky et al., 2019)or

assessing the prevalence of ASD among children with

CAS (e.g., Tierney et al., 2015; Vashdi et al., 2020) will

likely report different findings than studies in which the

participants were more verbal (e.g., Kjelgaard & Tager-

Flusberg, 2001).

Children with ASD are known to demonstrate a vari-

ety of speech sound errors, including both age-

appropriate phonological processes and atypical, age-

inappropriate errors (Cleland et al., 2010; Rapin

et al., 2009; Shriberg et al., 2011) as well as a potentially

higher prevalence of childhood apraxia of speech

(Chenausky et al., 2019; Tierney et al., 2015) and abnor-

malities of rate, coordination, and other features as

described in this review. The various classifications of

speech sound disorders and motor speech disorders are

not always clearly delineated, with significant symptoms

(e.g., consonant imprecision, prosodic errors) overlap-

ping between disorders. These similarities necessitate

careful and often challenging differential diagnosis

(Allison et al., 2020; Iuzzini-Seigel et al., 2022). We

excluded studies from this review if subjects had comor-

bidities with the potential to impact oromotor behaviors

(e.g., diagnosed hearing loss, cerebral palsy); however,

we cannot account for the real possibility that autistic

individuals may have more than one speech disorder, any

of which may occur in combination with the others.

Finally, behavioral research regarding ASD requires

consideration of the significant heterogeneity known to

characterize the population. This heterogeneity has been

observed in domains such as language (Rapin, 2006;

Tager-Flusberg, 2006) and IQ (Mayes & Calhoun, 2003),

as well at the levels of genetics (Szatmari, 1999) and neu-

roimaging (Martinez-Murcia et al., 2017). Changing

diagnostic criteria have led to a large increase in the

reported prevalence of ASD over the last several decades,

contributing further to the broad range of phenotypes

associated with the diagnosis of ASD. This variance has

important impacts both within studies (i.e., researchers

must ensure a representative sample of individuals with

ASD) and across studies. Studies of behavior in ASD

often constrain their sample using strict inclusion criteria

meant to create clean experimental groups and to answer

specific research questions. For instance, subgroups of

children with ASD have been selectively researched based

on impaired intelligibility (e.g., Koegel et al., 1998), a

diagnosis of CAS (e.g., Chenausky et al., 2020), or expo-

sure only to English at home (Schoen et al., 2011). A sig-

nificant trade-off of this approach is that it limits cross-

study comparison by not capturing the full range of

behaviors that characterize individuals with ASD.

Future research

The findings of this scoping review are intended to

inform and motivate future research. First, there is a rela-

tive dearth of instrumental studies compared to percep-

tual studies investigating oromotor functioning in ASD,

despite the wide availability and affordability of acoustic

analysis hardware and software. Instrumental methods

are objective, replicable, and sensitive to small differences

and changes over time compared to perceptual methods,

which have known limitations discussed above and which

involve intensive listener training. Second, while task

selection must be motivated by the specific research ques-

tions of a particular project, there appear to be relatively

few studies that compare the performance of autistic chil-

dren on differential tasks (e.g., real words vs. nonwords,

speech vs. nonspeech, single words vs. connected speech,

etc.). This is particularly important not only to specify

how oromotor impairments manifest, but also because

tasks often do not generalize to each other. For instance,

articulation tests and conversational speech samples can

provide significantly different accuracy profiles

(Morrison & Shriberg, 1992) and nonspeech oral motor

movements may not be associated with speech accuracy

(McCauley et al., 2009); future studies should consider

task selection in this context. Third, to further investigate

the correlations between oromotor performance and cog-

nitive, behavioral, or linguistic skills, further longitudinal

work should be performed to examine these relationships

over time. Finally, as discussed above, the current lack of

consensus on the nature of oromotor impairment among

autistic individuals suggests the possibility that the char-

acteristics of such an impairment (or multiple impairment

profiles) may fall outside existing classifications of speech

disorders. Future research with large samples of autistic

individuals may help to further elucidate this ongoing

MAFFEI ET AL.31

question and contribute to our understanding of this

complex topic.

Strengths and limitations

The strengths of this review include the wide-reaching

search strategy, which covered multiple large databases

and included peer-reviewed publications as well as Ph.D.

dissertations and book chapters. The inclusion of only

studies that met our criteria for reporting ASD diagnosis

also helped focus the findings of this review and increase

the generalizability of results to the population of individ-

uals diagnosed with ASD. On the other hand, this strict

adherence to our criteria (e.g., excluding studies in which

the method of ASD diagnosis was not described, or

which examined motor behaviors like phonation which

are closely related to oromotor function) may be consid-

ered a limitation since numerous relevant studies were

excluded and valuable data may have been lost in the

process. An additional limitation inherent to this scoping

review was the significant heterogeneity in study designs,

assessment tools, sample characteristics, and control

groups used in the included studies, which made cross-

study comparison and synthesis difficult.

Clinical implications

Findings from this scoping review demonstrate that,

despite methodological limitations and remaining gaps in

knowledge, there appear to be significant differences in

oromotor skills between autistic individuals and their

peers. This has multiple significant clinical implications

related to intervention and assessment in this population.

First, findings of existing and future research may moti-

vate the use of motor-based interventions to complement

linguistic approaches to expressive language therapy for

autistic individuals. Second, this review demonstrates

that there exists promising evidence for oromotor func-

tioning as a useful predictor of expressive language,

which would have a significant impact on the assessment

process of autistic children.

Summary

Research regarding oromotor functioning among autistic

individuals is critical to a fuller understanding of ASD,

with direct implications for early diagnosis of language

impairments, the identification of neurobiological mecha-

nisms influencing communication development, and the

creation or modification of language interventions. The

majority of studies in this review reported abnormal oro-

motor skills among autistic individuals, although inter-

pretation and generalization of these findings are

impacted by methodological limitations. A subset of

available evidence suggests that oromotor skills are

correlated with a variety of cognitive and behavioral abil-

ities, particularly expressive and receptive language skills.

Suggestions are provided for future research, which may

overcome existing limitations and further clarify this

important topic.

ACKNOWLEDGMENTS

This work was supported by National Institute on Deaf-

ness and Other Communication Disorders Grants P50

DC018006, awarded to Helen Tager-Flusberg, support-

ing Karen V. Chenausky and Jordan R. Green; F31

DC020108, awarded to Marc F. Maffei; R00 DC017490,

awarded to Karen V. Chenausky; and K24 DC016312,

awarded to Jordan R. Green. We thank Jessica Bell at

the MGH Institute of Health Professions library for her

assistance in designing the search procedure for this

review. Our sincere thanks go to the researchers who con-

ducted the studies included in this review and, in particu-

lar, to the study participants and their families.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are avail-

able from the corresponding author upon reasonable

request.

ORCID

Marc F. Maffei https://orcid.org/0000-0001-6341-1702

Jordan R. Green https://orcid.org/0000-0002-1464-1373

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Exploring Motor Speech Disorders in Low and Minimally Verbal Autistic Individuals: An Auditory-Perceptual Analysis

Article

Mar 2024
AM J SPEECH-LANG PAT

Purpose Motor deficits are widely documented among autistic individuals, and speech characteristics consistent with a motor speech disorder have been reported in prior literature. We conducted an auditory-perceptual analysis of speech production skills in low and minimally verbal autistic individuals as a step toward clarifying the nature of speech production impairments in this population and the potential link between oromotor functioning and language development. Method Fifty-four low or minimally verbal autistic individuals aged 4–18 years were video-recorded performing nonspeech oromotor tasks and producing phonemes, syllables, and words in imitation. Three trained speech-language pathologists provided auditory perceptual ratings of 11 speech features reflecting speech subsystem performance and overall speech production ability. The presence, attributes, and severity of signs of oromotor dysfunction were analyzed, as were relative performance on nonspeech and speech tasks and correlations between perceptual speech features and language skills. Results and Conclusions Our findings provide evidence of a motor speech disorder in this population, characterized by perceptual speech features including reduced intelligibility, decreased consonant and vowel precision, and impairments of speech coordination and consistency. Speech deficits were more associated with articulation than with other speech subsystems. Speech production was more impaired than nonspeech oromotor abilities in a subgroup of the sample. Oromotor deficits were significantly associated with expressive and receptive language skills. Findings are interpreted in the context of known characteristics of the pediatric motor speech disorders childhood apraxia of speech and childhood dysarthria. These results, if replicated in future studies, have significant potential to improve the early detection of language impairments, inform the development of speech and language interventions, and aid in the identification of neurobiological mechanisms influencing communication development.

Receptive language and receptive‐expressive discrepancy in minimally verbal autistic children and adolescents

Article

Dec 2023

Among the approximately one‐third of autistic individuals who experience considerable challenges in acquiring spoken language and are minimally verbal (MV), relatively little is known about the range of their receptive language abilities. This study included 1579 MV autistic children and adolescents between 5 and 18 years of age drawn from the National Database for Autism Research and the SFARI Base data repository. MV autistic children and adolescents demonstrated significantly lower receptive language compared to the norms on standardized language assessment and parent report measures. Moreover, their receptive language gap widened with age. Overall, our sample demonstrated significantly better receptive than expressive language. However, at the individual level, only about 25% of MV autistic children and adolescents demonstrated significantly better receptive language relative to their minimal expressive levels. Social skills explained a significant proportion of the variance in parent‐reported receptive language skills, while motor skills were the most significant predictor of greater receptive‐expressive discrepancy. Findings from this study revealed the heterogeneous language profiles in MV autistic children and adolescents, underscoring the importance of individualizing interventions to match their different communication strengths and needs and integrating multiple interconnected areas to optimize their overall development of language comprehension, socialization, and general motor skills.

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May 2024
Comput Speech Lang

OSMSE-E: Oral Speech Mechanism Screening Examination-Third Edition

Cover Page

Full-text available

Jan 2000

Imitation Performance in Children with Autism and the Role of Visual Attention in Imitation

Article

Full-text available

Sep 2022
J AUTISM DEV DISORD

In this study, we examined imitation performance, visual attention, and the relationship between imitation and visual attention of children with autism, developmental delay (DD), and typically developing (TD) children. The study findings revealed that children with autism and DD imitated less than TD children in all imitation tasks. Results also showed that children with autism spent less time looking at the model's face and movement area and more time looking at the external area. Lastly, the relationship between imitation and visual attention separated the study groups. The findings of the study provided new evidence that visual attention to movement area in children with autism was positively related to imitation performance in non-meaningful gestures.

Perceptual Classification of Motor Speech Disorders: The Role of Severity, Speech Task, and Listener's Expertise

Article

Full-text available

Jul 2022

Purpose The clinical diagnosis of motor speech disorders (MSDs) is mainly based on perceptual approaches. However, studies on perceptual classification of MSDs often indicate low classification accuracy. The aim of this study was to determine in a forced-choice dichotomous decision-making task (a) how accuracy of speech-language pathologists (SLPs) in perceptually classifying apraxia of speech (AoS) and dysarthria is impacted by speech task, severity of MSD, and listener's expertise and (b) which perceptual features they use to classify. Method Speech samples from 29 neurotypical speakers, 14 with hypokinetic dysarthria associated with Parkinson's disease (HD), 10 with poststroke AoS, and six with mixed dysarthria associated with amyotrophic lateral sclerosis (MD-FlSp [combining flaccid and spastic dysarthria]), were classified by 20 expert SLPs and 20 student SLPs. Speech samples were elicited in spontaneous speech, text reading, oral diadochokinetic (DDK) tasks, and a sample concatenating text reading and DDK. For each recorded speech sample, SLPs answered three dichotomic questions following a diagnostic approach, (a) neurotypical versus pathological speaker, (b) AoS versus dysarthria, and (c) MD-FlSp versus HD, and a multiple-choice question on the features their decision was based on. Results Overall classification accuracy was 72% with good interrater reliability, varying with SLP expertise, speech task, and MSD severity. Correct classification of speech samples was higher for speakers with dysarthria than for AoS and higher for HD than for MD-FlSp. Samples elicited with continuous speech reached the best classification rates. An average number of three perceptual features were used for correct classifications, and their type and combination differed between the three MSDs. Conclusions The auditory-perceptual classification of MSDs in a diagnostic approach reaches substantial performance only in expert SLPs with continuous speech samples, albeit with lower accuracy for AoS. Specific training associated with objective classification tools seems necessary to improve recognition of neurotypical speech and distinction between AoS and dysarthria.

Auditory‐motor mapping training: Testing an intonation‐based spoken language treatment for minimally verbal children with autism spectrum disorder

Article

Full-text available

Jun 2022
ANN NY ACAD SCI

We tested an intonation‐based speech treatment for minimally verbal children with autism (auditory‐motor mapping training, AMMT) against a nonintonation–based control treatment (speech repetition therapy, SRT). AMMT involves singing, rather than speaking, two‐syllable words or phrases. In time with each sung syllable, therapist and child tap together on electronic drums tuned to the same pitches, thus coactivating shared auditory and motor neural representations of manual and vocal actions, and mimicking the “babbling and banging” stage of typical development. Fourteen children (three females), aged 5.0–10.8, with a mean Autism Diagnostic Observation Schedule‐2 score of 22.9 (SD = 2.5) and a mean Kaufman Speech Praxis Test raw score of 12.9 (SD = 13.0) participated in this trial. The main outcome measure was percent syllables approximately correct. Four weeks post‐treatment, AMMT resulted in a mean improvement of +12.1 (SE = 3.8) percentage points, compared to +2.8 (SE = 5.7) percentage points for SRT. This between‐group difference was associated with a large effect size (Cohen's d = 0.82). Results suggest that simultaneous intonation and bimanual movements presented in a socially engaging milieu are effective factors in AMMT and can create an individualized, interactive music‐making environment for spoken‐language learning in minimally verbal children with autism.

Cross-linguistic patterns of speech prosodic differences in autism: A machine learning study

Article

Full-text available

Jun 2022
PLOS ONE

Differences in speech prosody are a widely observed feature of Autism Spectrum Disorder (ASD). However, it is unclear how prosodic differences in ASD manifest across different languages that demonstrate cross-linguistic variability in prosody. Using a supervised machine-learning analytic approach, we examined acoustic features relevant to rhythmic and intonational aspects of prosody derived from narrative samples elicited in English and Cantonese, two typologically and prosodically distinct languages. Our models revealed successful classification of ASD diagnosis using rhythm-relative features within and across both languages. Classification with intonation-relevant features was significant for English but not Cantonese. Results highlight differences in rhythm as a key prosodic feature impacted in ASD, and also demonstrate important variability in other prosodic properties that appear to be modulated by language-specific differences, such as intonation.

Review of methods for conducting speech research with minimally verbal individuals with autism spectrum disorder

Article

Nov 2022
AUGMENT ALTERN COMM

The purpose of this paper was to review best-practice methods of collecting and analyzing speech production data from minimally verbal autistic speakers. Data on speech production data in minimally verbal individuals are valuable for a variety of purposes, including phenotyping, clinical assessment, and treatment monitoring. Both perceptual ("by ear") and acoustic analyses of speech can reveal subtle improvements as a result of therapy that may not be apparent when correct/incorrect judgments are used. Key considerations for collecting and analyzing speech production data from this population are reviewed. The definition of "minimally verbal" that is chosen will vary depending on the specific hypotheses investigated, as will the stimuli to be collected and the task(s) used to elicit them. Perceptual judgments are ecologically valid but subject to known sources of bias; therefore, training and reliability procedures for perceptual analyses are addressed, including guidelines on how to select vocalizations for inclusion or exclusion. Factors to consider when recording and acoustically analyzing speech are also briefly discussed. In summary, the tasks, stimuli, training methods, analysis type(s), and level of detail that yield the most reliable data to answer the question should be selected. It is possible to obtain rich high-quality data even from speakers with very little speech output. This information is useful not only for research but also for clinical decision-making and progress monitoring.

A Combined Spoken Language Intervention for a Minimally Verbal Child With Autism: A Clinical Case Study

Article

Jun 2022

Purpose The purpose of this study was to explore the effect of a combined intervention program, incorporating both communication and speech production strategies, on the spoken language of a minimally verbal child with autism spectrum disorder (ASD). Method This in-depth, clinical case study focused on a 4-year-old boy who was diagnosed with ASD. The study examined the spoken production of preselected words by the participant, using a combined intervention approach, consisting of three communication strategies and three speech production strategies during structured play. The target words were chosen based on aspects of neurotypical word learning during the first 50-word phase of language development. The combined intervention approach consisted of three communication strategies and three speech production strategies delivered during structured play activities. Results The participant demonstrated improved spontaneous and imitative production of the target words during treatment and maintained production of the target words 1 month after treatment ended. Additionally, the boy demonstrated production of seven out of the eight target words on the posttreatment MacArthur–Bates Communicative Development Inventories (CDI): Words and Gestures, as well as an increase in the number of spoken words on the CDI that were more than what would be expected for a neurotypical child in a comparable developmental period. The communication strategy used most often was modeling (which required the child to respond), and the speech production strategy used most often was verbal modeling (which did not require the child to respond). Conclusions There appeared to be a quantitative and qualitative difference in production of the target words before and after treatment. Clinical implications for using communication and speech production strategies in combination for minimally verbal children with ASD are discussed.

Validity of Off-the-Shelf Automatic Speech Recognition for Assessing Speech Intelligibility and Speech Severity in Speakers With Amyotrophic Lateral Sclerosis

Article

May 2022
J SPEECH LANG HEAR R

Purpose: There is increasing interest in using automatic speech recognition (ASR) systems to evaluate impairment severity or speech intelligibility in speakers with dysarthria. We assessed the clinical validity of one currently available off-the-shelf (OTS) ASR system (i.e., a Google Cloud ASR API) for indexing sentence-level speech intelligibility and impairment severity in individuals with amyotrophic lateral sclerosis (ALS), and we provided guidance for potential users of such systems in research and clinic. Method: Using speech samples collected from 52 individuals with ALS and 20 healthy control speakers, we compared word recognition rate (WRR) from the commercially available Google Cloud ASR API (Machine WRR) to clinician-provided judgments of impairment severity, as well as sentence intelligibility (Human WRR). We assessed the internal reliability of Machine and Human WRR by comparing the standard deviation of WRR across sentences to the minimally detectable change (MDC), a clinical benchmark that indicates whether results are within measurement error. We also evaluated Machine and Human WRR diagnostic accuracy for classifying speakers into clinically established categories. Results: Human WRR achieved better accuracy than Machine WRR when indexing speech severity, and, although related, Human and Machine WRR were not strongly correlated. When the speech signal was mixed with noise (noise-augmented ASR) to reduce a ceiling effect, Machine WRR performance improved. Internal reliability metrics were worse for Machine than Human WRR, particularly for typical and mildly impaired severity groups, although sentence length significantly impacted both Machine and Human WRRs. Conclusions: Results indicated that the OTS ASR system was inadequate for early detection of speech impairment and grading overall speech severity. While Machine and Human WRR were correlated, ASR should not be used as a one-to-one proxy for transcription speech intelligibility or clinician severity ratings. Overall, findings suggested that the tested OTS ASR system, Google Cloud ASR, has limited utility for grading clinical speech impairment in speakers with ALS.

A Tool for Differential Diagnosis of Childhood Apraxia of Speech and Dysarthria in Children: A Tutorial

Article

May 2022

Purpose While there has been mounting research centered on the diagnosis of childhood apraxia of speech (CAS), little has focused on differentiating CAS from pediatric dysarthria. Because CAS and dysarthria share overlapping speech symptoms and some children have both motor speech disorders, differential diagnosis can be challenging. There is a need for clinical tools that facilitate assessment of both CAS and dysarthria symptoms in children. The goals of this tutorial are to (a) determine confidence levels of clinicians in differentially diagnosing dysarthria and CAS and (b) provide a systematic procedure for differentiating CAS and pediatric dysarthria in children. Method Evidence related to differential diagnosis of CAS and dysarthria is reviewed. Next, a web-based survey of 359 pediatric speech-language pathologists is used to determine clinical confidence levels in diagnosing CAS and dysarthria. Finally, a checklist of pediatric auditory–perceptual motor speech features is presented along with a procedure to identify CAS and dysarthria in children with suspected motor speech impairments. Case studies illustrate application of this protocol, and treatment implications for complex cases are discussed. Results The majority (60%) of clinician respondents reported low or no confidence in diagnosing dysarthria in children, and 40% reported they tend not to make this diagnosis as a result. Going forward, clinicians can use the feature checklist and protocol in this tutorial to support the differential diagnosis of CAS and dysarthria in clinical practice. Conclusions Incorporating this diagnostic protocol into clinical practice should help increase confidence and accuracy in diagnosing motor speech disorders in children. Future research should test the sensitivity and specificity of this protocol in a large sample of children with varying speech sound disorders. Graduate programs and continuing education trainings should provide opportunities to practice rating speech features for children with dysarthria and CAS. Supplemental Material https://doi.org/10.23641/asha.19709146

Specificity of phonological representations in school-age high-functioning ASD children

Article

May 2022

Purpose: Well-specified phonological representations are important for the development of spoken and written language. This study investigates the types of speech errors and the quality of phonological representations in Greek-speaking school-age children with high-functioning autism spectrum disorder (HF-ASD), as well as the relationship between stored phonological representations and speech output in this sample, according to Stackhouse and Wells’ (1997) model. Method: All participants completed a phonological and a naming test, and a non-word repetition task. A receptive phonological task was administered to a subgroup of HF-ASD and typically developing (TD) participants. According to performance in the phonological test, the HF-ASD children were categorised as ASD with Speech Sound Disorder (SSD) or ASD without SSD. Result: The HF-ASD children made significantly more speech errors and showed significant difficulties in the repetition of non-words and the stored phonological representations compared to the TD group but had the same naming abilities with their TD peers. The ASD children with SSD and without SSD performed alike in the receptive task, indicating that both groups had unspecified phonological representations. Conclusion: These results support the hypothesis of distinct phonological representations for speech input and output and highlight the need of using receptive tasks to evaluate underlying phonological knowledge, a process which could allow clinicians to identify the level of speech breakdown.

Oromotor skills in autism spectrum disorder: A scoping review

Abstract

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