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Statistics of pauses appearing in Polish as a potential source of biometry information for automatic speaker recognition were described. The usage of three main types of acoustic pauses (silent, filled and breath pauses) and syntactic pauses (punctuation marks in speech transcripts) was investigated quantitatively in three types of spontaneous spee...
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The performances of the automatic speaker verification (ASV) systems degrade due to the reduction in the amount of speech used for enrollment and verification. Combining multiple systems based on different features and classifiers considerably reduces speaker verification error rate with short utterances. This work attempts to incorporate supplemen...
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... (9) what is this feeling Друга строфа. Рядки (10) what is the name for (11) how did I get here (12) This quiet work, Третя строфа. Рядки (13) ...
У вільному вірші рядок виконує функцію ритмічної одиниці. Довжина рядків, їх просторове розташування на сторінці, кількість рядків у строфі, наявність/відсутність розділових знаків залежать від авторського розуміння їх смислового навантаження. При читанні вільного вірша вголос пауза як просодичний засіб членування мовленнєвого потоку на смислові одиниці є одним з індикаторів інтерпретації твору. У статті викладені результати міні-проєкту, до якого в якості дикторів та аудиторів були залучені студенти старших курсів факультету іноземних мов Прикарпатського національного університету імені Василя Стефаника. Метою проєкту було дослідження ролі паузи в усних читацьких репрезентаціях вільного вірша “The long hall” сучасної ірландсько-канадійської авторки Стіві Хауелл. Перший аудіозапис був зроблений до здійснення учасниками проєкту аналізу твору за методикою теорії світу тексту, яка передбачає створення ментальних репрезентацій на основі текстової інформації та знань читачів про реальний світ. Застосування принципів теорії світу тексту сприяло процесам інтерпретації дискурсу вірша, чим пояснюються відмінності між першим і другим аудіозаписами. Аудитивний аналіз засвідчує, що створення ментальних репрезентацій, світів тексту вірша, змінює ракурс сприйняття дискурсу. У другому аудіозаписі, на відміну від першого, помітна тенденція до перенесення уваги з граматико-синтаксичного на смисловий аспект твору. При другому прочитанні це знайшло відображення у кореляції пауз – їх місця розташування, характеру і тривалості – з авторським розподілом тексту між рядками, що подекуди суперечить граматичним нормам, але створює своєрідний ритм поезії, передає смислове навантаження кожного рядка і посилює емоційний вплив на слухача.
... Speech Segmentation: A constant stream of 1-second chunks of your audio call is extracted during this stage. There is usually only one speaker in each speech section [20]. Embedding Extraction: Using a neural network, this system embeds the speech segments that were retrieved in the preceding phase. ...
Speak more than 15001 different languages in India. Vocal and linguistic technology would be quite beneficial to most of them. Indian languages have unique features and similarities that must be used in order to provide voice recognition capability for these low-resource languages. A low-resource Automatic Speech Recognition system for Indian languages was developed with this objective in mind. Rapid progress has been achieved in speaker diarization across a variety of application domains. A historical assessment of speaker diarization technology, as well as contemporary advances in neural speaker diarization techniques, is presented in this study. By integrating recent advancements in neural techniques. We believe that this study significantly contributes to the community by providing valuable insights and paving the way for advancements in the field of speaker diarization, ultimately leading to more efficient and accurate results.
... Speech Segmentation: A constant stream of 1-second chunks of your audio call is extracted during this stage. There is usually only one speaker in each speech section [20]. Embedding Extraction: Using a neural network, this system embeds the speech segments that were retrieved in the preceding phase. ...
Speak more than 15001 different languages in India. Vocal and linguistic technology would be quite beneficial to most of them. Indian languages have unique features and similarities that must be used in order to provide voice recognition capability for these low-resource languages. A low-resource Automatic Speech Recognition system for Indian languages was developed with this objective in mind. Rapid progress has been achieved in speaker diarization across a variety of application domains. A historical assessment of speaker diarization technology, as well as contemporary advances in neural speaker diarization techniques, is presented in this study. By integrating recent advancements in neural techniques. We believe that this study significantly contributes to the community by providing valuable insights and paving the way for advancements in the field of speaker diarization, ultimately leading to more efficient and accurate results.
... Although MFCC are the most widely employed domain for SR, especially with the introduction of the concept of i-vectors, it is undoubtable that many other features can indeed define the peculiarities of a speaker's voice. F0 and its variations are deeply related to intonation, whereas the amount of short breathing pauses, evaluated by means of the Voicing Probability, are shown to be able to classify between speakers [32]. The importance of said features is confirmed by the results of the feature selection, that shows the number of selected features being 3.45% of the original number. ...
Speaker Recognition (SR) is a common task in AI-based sound analysis, involving structurally different methodologies such as Deep Learning or “traditional” Machine Learning (ML). In this paper, we compared and explored the two methodologies on the DEMoS dataset consisting of 8869 audio files of 58 speakers in different emotional states. A custom CNN is compared to several pre-trained nets using image inputs of spectrograms and Cepstral-temporal (MFCC) graphs. AML approach based on acoustic feature extraction, selection and multi-class classification by means of a Naïve Bayes model is also considered. Results show how a custom, less deep CNN trained on grayscale spectrogram images obtain the most accurate results, 90.15% on grayscale spectrograms and 83.17% on colored MFCC. AlexNet provides comparable results, reaching 89.28% on spectrograms and 83.43% on MFCC.The Naïve Bayes classifier provides a 87.09% accuracy and a 0.985 average AUC while being faster to train and more interpretable. Feature selection shows how F0, MFCC and voicing-related features are the most characterizing for this SR task. The high amount of training samples and the emotional content of the DEMoS dataset better reflect a real case scenario for speaker recognition, and account for the generalization power of the models.
... Speech pauses in conversations have important communicative functions and can allow interlocutors to make inferences about the nature of the conversation. For example, interlocutors can infer communication style, the emotional state of the speaker or speaker identity by evaluating the pause characteristics of a conversation (e.g., Fletcher 2010;Igras-Cybulska et al. 2016;Duez 1982;Çokal et al. 2019;O'Connell and Kowal 1983;Lundholm-Fors 2015). Pauses also allow interlocutors to make inferences about their communication partners' cognitive state, knowledge and intentions. ...
Speech pauses between turns of conversations are crucial for assessing conversation partners’ cognitive states, such as their knowledge, confidence and willingness to grant requests; in general, speakers making longer pauses are regarded as less apt and willing. However, it is unclear if the interpretation of pause length is mediated by the accent of interactants, in particular native versus non-native accents. We hypothesized that native listeners are more tolerant towards long pauses made by non-native speakers than those made by native speakers. This is because, in non-native speakers, long pauses might be the result of prolonged cognitive processing when planning an answer in a non-native language rather than of a lack of knowledge, confidence or willingness. Our experiment, in which 100 native Polish-speaking raters rated native and non-native speakers of Polish on their knowledge, confidence and willingness, showed that this hypothesis was confirmed for perceived willingness only; non-native speakers were regarded as equally willing to grant requests, irrespective of their inter-turn pause durations, whereas native speakers making long pauses were regarded as less willing than those making short pauses. For knowledge and confidence, we did not find a mediating effect of accent; both native and non-native speakers were rated as less knowledgeable and confident when making long pauses. One possible reason for the difference between our findings on perceived willingness to grant requests versus perceived knowledge and confidence is that requests might be more socially engaging and more directly relevant for interpersonal cooperative interactions than knowledge that reflects on partners’ competence but not cooperativeness. Overall, our study shows that (non-)native accents can influence which cognitive states are signaled by different pause durations, which may have important implications for intercultural communication settings where topics are negotiated between native and non-native speakers.
... Measuring the amount of silent pauses in human speech is quite common (see e.g. Mattys et al., 2005;Fraser et al., 2013;Igras-Cybulska et al., 2016;Al-Ghazali and Alrefaee, 2019;Sluis et al., 2020). The attribute set developed by our team, besides silent pauses, also summarizes the amount of filled pauses (i.e. ...
Dementia is a chronic or progressive clinical syndrome, characterized by the deterioration of problem-solving skills, memory and language. In Mild Cognitive Impairment (MCI), which is often considered to be the prodromal stage of dementia, there is also a subtle deterioration of these cognitive functions; however, it does not affect the patients’ ability to carry out simple everyday activities. The timely identification of MCI could provide more effective therapeutic interventions to delay progression, and to postpone the possible conversion to dementia. Since language changes in MCI are present even before the manifestation of other distinctive cognitive symptoms, a non-invasive way of early automatic screening could be the use of speech analysis. Earlier, our research team developed a set of temporal speech parameters that mainly focus on the amount of silence and hesitation, and demonstrated its applicability for MCI detection. However, for the automatic extraction of these attributes, the execution of a full Automatic Speech Recognition (ASR) process is necessary. In this study we propose a simpler feature extraction approach, which still quantifies the amount of silence and hesitation in the speech of the subject, but does not require the application of a full ASR system. We experimentally demonstrate that this approach, operating directly on the frame-level output of a HMM/DNN hybrid acoustic model, is capable of extracting attributes as useful as the ASR-based temporal parameter extraction workflow was able to. That is, on our corpus consisting of 25 healthy controls, 25 MCI and 25 mild AD subjects, we achieve a (three-class) classification accuracy of 70.7%, an F-measure score of 89.6 and a mean AUC score of 0.804. We also show that this approach can be applied on simpler, context-independent acoustic states with only a slight degradation of MCI and mild Alzheimer’s detection performance. Lastly, we investigate the usefulness of the three speaker tasks which are present in our recording protocol.
... Finally, it should be acknowledged that "silent pause duration" may be efficient when used for differentiating different speaking styles. In the study conducted by [75] with Polish, on the application of pauses as a potential source of biometry for automatic speaker recognition, three types of acoustic pauses (silent, filled and breath pauses) and syntactic pauses were analyzed in both spontaneous and read speech. The researchers found that quantity and duration of filled pauses, audible breaths, and correlation between the temporal structure of speech and the syntactic structure were the best performing features for speaker characterization. ...
The purpose of this study was to assess the speaker-discriminatory potential of a set of speech timing parameters while probing their suitability for forensic speaker comparison applications. The recordings comprised of spontaneous dialogues between twin pairs through mobile phones while being directly recorded with professional headset microphones. Speaker comparisons were performed with twins speakers engaged in a dialogue (i.e., intra-twin pairs) and among all subjects (i.e., cross-twin pairs). The participants were 20 Brazilian Portuguese speakers, ten male identical twin pairs from the same dialectal area. A set of 11 speech timing parameters was extracted and analyzed, including speech rate, articulation rate, syllable duration (V-V unit), vowel duration, and pause duration. Three system performance estimates were considered for assessing the suitability of the parameters for speaker comparison purposes, namely global Cllr, EER, and AUC values. These were interpreted while also taking into consideration the analysis of effect sizes. Overall, speech rate and articulation rate were found the most reliable parameters, displaying the largest effect sizes for the factor "speaker" and the best system performance outcomes, namely lowest Cllr, EER, and highest AUC values. Conversely, smaller effect sizes were found for the other parameters, which is compatible with a lower explanatory potential of the speaker identity on the duration of such units and a possibly higher linguistic control regarding their temporal variation. In addition, there was a tendency for speech timing estimates based on larger temporal intervals to present larger effect sizes and better speaker-discriminatory performance. Finally, identical twin pairs were found remarkably similar in their speech temporal patterns at the macro and micro levels while engaging in a dialogue, resulting in poor system discriminatory performance. Possible underlying factors for such a striking convergence in identical twins' speech timing patterns are presented and discussed.
... In these approaches, the content of a meeting is easily analyzed and sensitive data can be exploited. As some works have already shown [Kakita and Hiki, 2015], [Igras-Cybulska et al., 2016], silences, more particularly nonspeech segments contain enough information to determine the structure of a meeting or even characterize a speaker. This paper has the following structure: Section II presents the related work and Section III describes our approach. ...
... Studying silences can also help to characterize speakers. In [Igras-Cybulska et al., 2016], the authors propose to study the three main types of acoustic pauses (silent, filled and breath pauses), and syntactic pauses (punctuation marks in speech transcripts) for speaker recognition and speaker profile estimation. After a statistical study of the parameters they extracted, the authors showed that the quantity and duration of filled pauses, audible breaths, and the correlation between the temporal structure of speech and the syntactic structure of the utterances were the features that characterize speakers most. ...
... There are three types of speech pauses in spoken language silent pauses, filled pause, and breath pause (Igras-Cybulska, Ziółko, Ż elasko, & Witkowski, 2016). While filled pauses contain filler words such as "um," silent pause contains no voice activity. ...
... Breath pauses, which can be detected by high-quality voice recording devices, are pauses taken when a speaker stops to inhale and exhale (Igras & Ziolko, 2013). Research shows that breath pauses are significant indicators of punctuation in spoken language and are natural signals of sentence and phrase borders (Igras-Cybulska et al., 2016;Igras & Ziolko, 2013). In some research works, breath pauses are a sub-type of silent pauses. ...
... In some research works, breath pauses are a sub-type of silent pauses. Although silent pause can be indicative of problems in discourse and syntactic planning in spoken language (Rose, 2017), long silent pauses are often used as stylistic forms by professional speakers (Igras-Cybulska et al., 2016). Before a presentation, sentences, phrases, and topic boundaries in presentation slides and accompanying slide notes can be identified using natural language processing algorithms (Furui & Kawahara, 2008). ...
Most speakers experience public speaking anxiety in one form or another, ranging from slight nervousness to paralyzing fear and panic. Public speaking anxiety negatively affects the quality of most presentations, but few people seek out treatments for their anxiety. Real-time public speaking anxiety interventions that assist presenters manage their anxiety during a presentation can be of use to many presenters. Motivated by research on deep breathing interventions for stress management, we conduct a study with eleven participants to explore designs for deep breathing interventions that can be used during a presentation in front of an audience. We developed and compared three prototypes that nudged presenters to perform different actions (deep breathing, pausing, or smiling and looking at the audience) during a presentation. We report on participants’ experience interacting with the prototypes and our findings on how sensor-driven technologies can nudge presenters to perform deep breathing as a just-in-time technique for reducing anxiety. Our findings reveal that such interventions should be automatically triggered by presenter anxiety level, use visual prompts that do not obscure the presentation content and be delivered at topic boundaries.
... Hesitation markers are understood in this study in a narrower sense than hesitation phenomena classified for the purposes of conversation analysis or foreign-language learning, in which they range from pauses and syllable lengthening through repeats and restarts to small words and editing expressions (see e.g., Bortfeld et al. 2001;Gilquin 2008). In this article, then, hesitation markers refer to non-lexical fillers in the form of meaningless strings of prolonged sounds (in Polish, typically assuming the form of prolonged vowels 'yyy' , 'eee' or 'mmm'; Igras-Cybulska et al. 2016). They are also referred to in the literature as 'filled pauses' or 'fillers' (Bortfeld et al. 2001). ...
This article investigates the correlation between explicitation and increased cognitive load in simultaneous interpreting by trainee interpreters. It has been hypothesised, on the one hand, that certain explicitating shifts in simultaneous interpreting may be caused by increased cognitive load and they may be performed in an attempt to mask processing problems; and, on the other, that performing explicitating shifts may lead to increased cognitive load and trigger processing problems. The study triangulates product analysis (manual comparison of source and target texts) with process analysis (retrospective protocols of the participants). In the product the correlation between the occurrence of explicitating shifts and increased cognitive load is sought by identifying problem indicators in the form of three types of disfluency: hesitation markers, false starts and anomalous pauses exceeding two seconds (performance measure). Retrospective protocols are analysed in search of reports of explicitating shifts and/or increased cognitive load experienced and/or the cognitive effort expended (subjective measure). The product analysis shows the correlation between explicitating shifts and cognitive load at the level of 31%. The Spearman’s rank-order correlation coefficient r=0.48 indicates that there is a positive association between these two variables. This finding is further confirmed by 122 retrospective comments of the subjects in the study.
Keywords: simultaneous interpreting, explicitation, cognitive load, cognitive effort, process research, retrospective protocols
https://benjamins.com/catalog/intp.00051.gum
