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Vibration patterns of vocal cords representd by the one-mass model. A. Position of the right cord body mass (full line), and position of the left cord body mass (dot line). B. Right cord mass velocity. C. Speed of the body mass relative to its resting position (x 5 0). The vibration cycle is as follows: (1) Both left and right masses start separating from the resting position. (2) Maximum separation is reached, the relative speed becomes null, the cord tension inverts the movement, the velocity becomes negative in the right cord mass, and positive in the left cord mass. (3) Both masses come in close contact (possible collision effects) during a small fraction of time to separate again and start a new vibration cycle (4).
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Voice disorders are a source of increasing concern as normal voice quality is a social demand for at least one third of the population in developed countries in cases where voice is an essential resource in professional exercise. In addition, the growing exposure to certain pathogenic factors such as smoking, alcohol abuse, air pollution, and acous...
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Context 1
... equivalent global masses M gl,r of the vocal folds vibrate as a response to the force ( f cl,r ) ex- erted by the supraglottal and subglottal pressure differences. The vibration along the axis x will be described by the position of the global masses M gl,r at each instant ( Figure 3A). This movement when plotted versus time would be described by arch-like oscillation cycles, which are a result of ideally elastic cord collision, represented by the rectified sinusoids with frequencies u tl and u tr given by the resonant system composed by both cords as ...
Context 2
... resulting vibration patterns would be given by plots similar to the ones shown in Figure 3, these being the result of considering the cords as vibrat- ing freely, colliding once in a vocal cycle, and sep- arating again in a bouncing movement. The behavior of the cords between collisions would be given by the dynamics of the one-mass model as given in Equation 2. ...
Context 3
... templates shown in Figure 6 give a view of the results of the inverse filtering when applied to a specific voice trace ( Figure 6A). In Figure 6B, the correlate of the second derivative of the glottal source v g (n) is obtained (compare its resemblance with the trace in Figure 3C correspond- ing to sample case ''00B''). The integration of v g (n) results in the trace shown in Figure 6C, which should be associated with the glottal source correlate. ...
Context 4
... 27 The results presented here correspond to the subtraction of FIGURE 6. Signals involved in the inversion process: A. Voice sample from subject ''00B.'' B. Glottal source derivative correlate after the removal of the vocal tract from input voice, related with the relative speed between each cord center of masses-compare its behavior with the trace in Figure 3, C. C. Glottal source correlate showing a behavior corresponding to a stiffness slightly larger than expected (see the explanation given in the Results section). D. Glottal flow correlate as estimated from the integration of the glottal source correlate. ...
Context 5
... the case cor- responding to the figures in the sequel, the number of phonation cycles produced was 36. The first two parameters estimated are the pitch ( p 1 , Figure 13) and jitter ( p 2 , Figure 14). Parameters p 3-6 are three variants of shimmer estimated by different algo- rithms ( Figure 15). ...
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The dramatic impact of neurological degenerative pathologies in life quality is a growing concern nowadays. Many techniques have been designed for the detection, diagnosis, and monitoring of the neurological disease. Most of them are too expensive or complex for being used by primary attention medical services. On the other hand, it is well known t...
Citations
... One of the most used techniques is inverse filtering. In voice pathology detection, the vibratory dynamics of the vocal folds can be analyzed, removing the influence of vocal-tract resonances [33,34]. However, when dealing with genetic syndromes, vocalproperty alterations may not be uniquely associated with biomechanical factors of the vocal folds but also with several morphological anomalies that affect the vocal tract. ...
Perceptual and statistical evidence has highlighted voice characteristics of individuals affected by genetic syndromes that differ from those of normophonic subjects. In this paper, we propose a procedure for systematically collecting such pathological voices and developing AI-based automated tools to support differential diagnosis. Guidelines on the most appropriate recording devices, vocal tasks, and acoustical parameters are provided to simplify, speed up, and make the whole procedure homogeneous and reproducible. The proposed procedure was applied to a group of 56 subjects affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS), and Smith–Magenis syndrome (SMS). The entire database was divided into three groups: pediatric subjects (PS; individuals < 12 years of age), female adults (FA), and male adults (MA). In line with the literature results, the Kruskal–Wallis test and post hoc analysis with Dunn–Bonferroni test revealed several significant differences in the acoustical features not only between healthy subjects and patients but also between syndromes within the PS, FA, and MA groups. Machine learning provided a k-nearest-neighbor classifier with 86% accuracy for the PS group, a support vector machine (SVM) model with 77% accuracy for the FA group, and an SVM model with 84% accuracy for the MA group. These preliminary results suggest that the proposed method based on acoustical analysis and AI could be useful for an effective, non-invasive automatic characterization of genetic syndromes. In addition, clinicians could benefit in the case of genetic syndromes that are extremely rare or present multiple variants and facial phenotypes.
... 6 The literature is rich in various phonation models that can be used in lieu of the BCM, including refined lumped-element (Galindo et al. 2017;Alzamendi et al. 2020) and high-fidelity models Movahhedi et al. 2021). The BCM has been selected herein for its reasonable computational requirements and demonstrated capability to capture the essential physics of phonation in various studies, see for example Zañartu et al. (2014); Serry et al. (2021); Deng et al. (2019Deng et al. ( , 2022; Titze (2004); Lowell and Story (2006); Gómez-Vilda et al. (2007). 7 This is essentially the same collision model as for the hybrid phonation model. ...
Fundamental frequency patterns during phonation onset have received renewed interest due to their promising application in objective classification of normal and pathological voices. However, the associated underlying mechanisms producing the wide array of patterns observed in different phonetic contexts are not yet fully understood. Herein, we employ theoretical and numerical analyses in an effort to elucidate the potential mechanisms driving opposing frequency patterns for initial/isolated vowels versus vowels preceded by voiceless consonants. Utilizing deterministic lumped-mass oscillator models of the vocal folds, we systematically explore the roles of collision and muscle activation in the dynamics of phonation onset. We find that an increasing trend in fundamental frequency, as observed for initial/isolated vowels, arises naturally through a progressive increase in system stiffness as collision intensifies as onset progresses, without the need for time-varying vocal fold tension or changes in aerodynamic loading. In contrast, reduction in cricothyroid muscle activation during onset is required to generate the decrease in fundamental frequency observed for vowels preceded by voiceless consonants. For such phonetic contexts, our analysis shows that the magnitude of reduction in the cricothyroid muscle activation and the activation level of the thyroarytenoid muscle are potential factors underlying observed differences in (relative) fundamental frequency between speakers with healthy and hyperfunctional voices. This work highlights the roles of sometimes competing laryngeal factors in producing the complex array of observed fundamental frequency patterns during phonation onset.
... Despite being a recent tool, the Voice Clinical Systems is included in the list of validated applications for smartphones for clinical practice [8]. In addition, various investigations have shown the effectiveness of this tool for screening patients with vocal pathology [9,10,11,12,13]. ...
Introduction. Covid-19 is an infectious disease with a different symptomatic implication depending on each person. There are sequelae in the nervous, cardiovascular and / or digestive system that involve the approach and multidisciplinary work of different health professionals where the speech therapist is included. In this way, we can speak of a direct relationship between speech therapy and Covid-19; especially in those patients with
serious sequelae such as the inability to eat and / or speak and the loss of voice. The damage caused to the laryngeal mucosa triggers the loss of some of the qualities of the voice, limiting oral communication. That is why we can find dysphonias caused by a great weakness, by a continuous overexertion or because of a paralysis of the vocal cords. Objectives/Hypothesis. The objective of this study was to identify the patterns of behavior in the biomechanical correlates of people who passed Covid-19 symptomatically with sequelae in voice. Methods. An experimental study with a total of 21 participants (11 women and 10 men) with sequelae in voice post Covid-19 is presented. Voice samples were collected and biomechanical correlates were analyzed through the Voice Clinical Systems program. Results and Conclusions. The results show different altered biomechanical patterns between men and women that correlate with other infectious diseases.
... The movement of the VFs can be described as an interaction of the locomotion between its components, the body and the cover layer tissue. The dynamic of the cover layer while closing phase is characterized as a traveling wave [5]. Figure 1 shows a schematic illustration of a full cycle of the vibration between the VF while phonation. ...
During phonation, the vocal folds execute a complex movement. Related to the subglottal pressure and tension of the vocal folds themselves, this movement may differ, furthermore depending on the kind of phonation a collision between both vocal folds may occur. If the vocal folds are overstrained, this can cause lesions. To gain knowledge about the vocal folds movements and the way of their collisions, a high-speed evaluation unit for a pressure-mapping sensor was developed. The sensor brings along a local resolution of 27.6 measure points per square centimeter and with the developed unit an adjustable framerate up to 1 kHz can be realized. Due to a sensor thickness of about 0.2 mm, an integration into hemi-larynx flow experiments is quite simple without vigorous changes on the setup. Because of an unrestricted field of view it is possible to record the movements with a high-speed camera. These recordings will be correlated with contact pressures in the post processing. During these experiments, different flowrates of the excitation air.
... A voice disorder occurs when an individual expresses concern about having an abnormal voice (with reference to voice quality, pitch, or loudness) that does not meet daily needs-even if others do not perceive it as different or deviant [1,2]. Voice disorders are a source of increasing concern, as normal voice quality is a social demand [3]. ...
The production of voice is a powerful tool not only for communication, but also for artistic performances [...]
... They noticed that when the vocal subject had a higher level of chaotic and aperiodic, they saw an increase in the number of pitch mistakes. Another study conducted by Gomez-Velda et al [29] shows how they employ biomechanical measurements as characteristics. This feature was obtained from a sample of vocal sound and can estimate vocal fold displacement noninvasively. ...
The integration of artificial intelligence (AI) and the Internet of Things (IoT) has a great prospect in smart healthcare. The advancement of AI in the form of deep learning brought a revolution in automatic classification or detection systems. In addition, next-generation wireless communications such as 5G network brought speed and seamless transmission of data. By coupling all these ingredients, the smart healthcare sector is booming these days. Especially, during and the post-COVID-19 pandemic, the necessity of smart healthcare came to light more than before. A significant number of populations in the world suffer from voice pathology. This pathology can be easily cured if detected earlier. In this paper, a voice pathology detection system has been proposed in a smart healthcare framework. The inputs are obtained by IoTs, namely, microphone and electroglottography (EGG) device to capture voice and EGG signals, respectively. Spectrograms are obtained from these signals and fed into a pretrained convolutional neural network (CNN). The features extracted from the CNN are fused and processed by a bi-directional long-short term memory network. The proposed system is evaluated by using a publicly available database, the Saarbrucken voice database. Experimental results prove that bimodal input is better performing than a single input. An accuracy of 95.65% is obtained by the proposed system.
... • Opening instants tO1 and tO2, in which the vocal folds initiate the abduction and the pressure of the glottal source is increasing. • Maximum and minimum flow declination rates, tM and tm, which refers to the instant of maximum rise of the glottal flow and with the highest values of the glottal source [43,44]. Fig. 1 shows these biometric points of the glottal source cycle. ...
... The first four features (Fundamental frequency (pitch), Jitter, Shimmer, and NHR) will be used in testing if f0 in DS children is low, unstable and dysphonic, as observed in DS adults. Biomechanical features (35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46) are intended to estimate the mass and, overall, the stiffness in the body and the cover of the vocal folds, as well as their instability. All these characteristics provide much information, as they depend directly on neuromotor control. ...
Down Syndrome (DS) has been the aim of numerous studies, although its phonation specificities have not received much attention. DS is a genetic disorder caused by the presence of a third copy of chromosome 21, being the explanation of different behavioral and clinical anomalies: developmental disorders, hearing, speech and language impairments and neuromotor and morphological alterations of the oro-naso-pharyngeal (ONP) and laryngeal (ONL) structures. The goal of the current study is to carry on a first approach to the biomechanics of phonation in DS children from an acoustic perspective, trying to describe its specificities and to establish a link with the syndrome. A specific application designed to analyze the glottal source and its relationship to vocal fold biomechanics has been used. Nineteen biomechanical features from vocal fold dynamics have been used, related to fundamental frequency, jitter, shimmer, noise-to-harmonic ratio, mass and stiffness profiles, unbalance, contact gaps, and tremor. On one hand, the non-normophonic corpus is composed of eight children with DS with age range between five and six years old. On the other hand, a normative or normophonic population was taken. The last group consisted of eight children with similar ages to the previous ones. The results achieved show relevant differences (p < .05) in larynx biomechanics regarding the following features: deviations in the fundamental frequency, vocal fold tension (regarding musculus vocalis), and phonation tremor. These distortions could be directly related with the neuromotor alterations known to be associated to DS and could reflect underlying factors of DS phonation features.
... It is important to note that all cases with an abnormal high F0 presented excessive vocal muscle stiffness at the same time. This feature 37 estimates the vocal muscle stiffness resulting from an excess of strain in phonation (Gómez-Vilda et al. 2007;Gómez et al. 2009;Gómez et al. 2013a). This phenomenon could indicate a laryngeal hyperfunction in most of the cases studied. ...
... These are related to the dynamic mass of the vocal muscle and the cover of the vocal fold, respectively (Table 2). In particular, the results of 35 and 41 could be related to the estimation of the dynamic mass of the muscle and the cover of the vocal fold (Gómez-Vilda et al. 2007;Gómez et al. 2009;Gómez et al. 2013a). In these two features, the results are significantly below the reference of normality, that is, it could be said that almost all cases with SMS have a dynamic mass of the vocal fold well below the adult average, both male and female. ...
Smith–Magenis syndrome (SMS) is a rare genetic disease characterized by intellectual disability, serious behavior disorders, neurodevelopment delay, and speech and language disorders. An acoustic and biomechanical analysis of the voice of SMS young adults was carried out due to (a) the close relationship between the laryngeal biomechanics and the clinical and emotional state of a person; (b) the fact that no research on the voice in this syndrome has been conducted previously. The vocal timbre of most people diagnosed with SMS does not seem to be according to the complexion of diagnosed individuals, nor to their gender and age, so it could be interesting to attend the analysis of phonation of people with a rare genetic syndrome such as SMS. We used BioMetPhon, a specific piece of software to analyze the glottal source and biomechanics of vocals folds. Nineteen features related to dysphonia, physiology, and biomechanics of the vocal folds were considered. The adult phonation of 9 individuals with SMS was analyzed and compared to 100 normative male and female adult voices. Results showed that the phonation of the SMS group significantly deviates from the adult normophonic profile in more than one of the 19 features examined, such as stiffness of the thyroarytenoid muscle and dynamic mass of the vocal fold cover, among others.
... The biomechanical properties of the VFs change if pathologies occur [4]. To separate the nondysphonic cases from dysphonic, it was shown that the measurement of VFs stiffness is highly sensitive [5]. Different methods are used in vivo to determine the stiffness of the VFs: aspiration method [6], tension meter, optical measurements and ultrasound [7]. ...
The diagnosis of changes in the structure of the vocal folds is an important clinical task. There is no approach available, which is capable of getting synchronous spatial and biomechanical properties of them. Whereas biomechanical changes, particularly the elasticity, are a good maker for malignant and benign conditions. To gain the elastic characteristics of the vocal folds we are proposing an endoscopic approach using ultrasound elastography.
... Further, the understanding of the vocal folds' biomechanics and the voice forming process could benefit from it. biomechanical properties of the VFs [3,4]. Therefore, information about the biomechanics of the VFs is not only beneficiary for research, but also for clinical assessments. ...
... The measurement of the elastic properties of the VFs could be beneficial for diagnostic methods of voice disorders, but further research is necessary [3,4]. Moreover, the results of our study can be used in refining models of the VFs. ...
Vocal folds are an essential part of human voice production. The biomechanical properties are a good indicator for pathological changes. In particular, as an oscillation system, changes in the biomechanical properties have an impact on the vibration behavior. Subsequently, those changes could lead to voice-related disturbances. However, no existing examination combines biomechanical properties and spatial imaging. Therefore, we propose an image registration-based approach, using ultrasound in order to gain this information synchronously. We used a quasi-static load to compress the tissue and measured the displacement by image registration. The strain distribution was directly calculated from the displacement field, whereas the elastic properties were estimated by a finite element model. In order to show the feasibility and reliability of the algorithm, we tested it on gelatin phantoms. Further, by examining ex vivo porcine vocal folds, we were able to show the practicability of the approach. We displayed the strain distribution in the tissue and the elastic properties of the vocal folds. The results were superimposed on the corresponding ultrasound images. The findings are promising and show the feasibility of the suggested approach. Possible applications are in improved diagnosis of voice disorders, by measuring the biomechanical properties of the vocal folds with ultrasound. The transducer will be placed on the vocal folds of the anesthetized patient, and the elastic properties will be measured. Further, the understanding of the vocal folds’ biomechanics and the voice forming process could benefit from it.
