Relevant bibliographies by topics / Speech pause detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Speech pause detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Speech pause detection"

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ESPOSITO, ANNA, VOJTĚCH STEJSKAL, and ZDENĚK SMÉKAL. "COGNITIVE ROLE OF SPEECH PAUSES AND ALGORITHMIC CONSIDERATIONS FOR THEIR PROCESSING." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 05 (August 2008): 1073–88. http://dx.doi.org/10.1142/s0218001408006508.

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This study investigates pausing strategies, focusing the attention on empty speech pauses. A cross-modal analysis (video and audio) of spontaneous narratives produced by male and female children and adults showed that a remarkable amount of empty speech pauses was used to signal new concepts in the speech flow and to segment discourse units such as clauses and paragraphs. Based on these results, an adaptive mathematical model for pause distribution was suggested, that exploits, as pause features, the absence of signal and/or the changes of energy over different acoustic dimensions strongly related to the auditory perception. These considerations inspired the formulation and the implementation of two pause detection procedures that proved to be more effective than the Likelihood Ratio Test (LRT) and Long-Term Spectral Divergence (LTSD) algorithms recently proposed in literature and applied for Voice Activity Detection (VAD).
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Toth, Laszlo, Ildiko Hoffmann, Gabor Gosztolya, Veronika Vincze, Greta Szatloczki, Zoltan Banreti, Magdolna Pakaski, and Janos Kalman. "A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech." Current Alzheimer Research 15, no. 2 (January 3, 2018): 130–38. http://dx.doi.org/10.2174/1567205014666171121114930.

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Background: Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI. Methods: Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech signals, first manually (using the Praat software), and then automatically, with an automatic speech recognition (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features. Results: The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process – that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%. Conclusion: The temporal analysis of spontaneous speech can be exploited in implementing a new, automatic detection-based tool for screening MCI for the community.
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Mattys, Sven L., and Jamie H. Clark. "Lexical activity in speech processing: evidence from pause detection." Journal of Memory and Language 47, no. 3 (October 2002): 343–59. http://dx.doi.org/10.1016/s0749-596x(02)00037-2.

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Hamzah, Raseeda, Nursuriati Jamil, and Rosniza Roslan. "Development of Acoustical Feature Based Classifier Using Decision Fusion Technique for Malay Language Disfluencies Classification." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 1 (October 1, 2017): 262. http://dx.doi.org/10.11591/ijeecs.v8.i1.pp262-267.

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<p>Speech disfluency such as filled pause (FP) is a hindrance in Automated Speech Recognition as it degrades the accuracy performance. Previous work of FP detection and classification have fused a number of acoustical features as fusion classification is known to improve classification results. This paper presents new decision fusion of two well-established acoustical features that are zero crossing rates (ZCR) and speech envelope (ENV) with eight popular acoustical features for classification of Malay language filled pause (FP) and elongation (ELO). Five hundred ELO and 500 FP are selected from a spontaneous speeches of a parliamentary session and Naïve Bayes classifier is used for the decision fusion classification. The proposed feature fusion produced better classification performance compared to single feature classification with the highest F-measure of 82% for both classes.</p>
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Marzinzik, M., and B. Kollmeier. "Speech pause detection for noise spectrum estimation by tracking power envelope dynamics." IEEE Transactions on Speech and Audio Processing 10, no. 2 (2002): 109–18. http://dx.doi.org/10.1109/89.985548.

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Beritelli, Francesco, Salvatore Casale, and Salvatore Serrano. "A low-complexity speech-pause detection algorithm for communication in noisy environments." European Transactions on Telecommunications 15, no. 1 (January 2004): 33–38. http://dx.doi.org/10.1002/ett.943.

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Holzgrefe-Lang, Julia, Caroline Wellmann, Barbara Höhle, and Isabell Wartenburger. "Infants’ Processing of Prosodic Cues: Electrophysiological Evidence for Boundary Perception beyond Pause Detection." Language and Speech 61, no. 1 (September 22, 2017): 153–69. http://dx.doi.org/10.1177/0023830917730590.

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Infants as young as six months are sensitive to prosodic phrase boundaries marked by three acoustic cues: pitch change, final lengthening, and pause. Behavioral studies suggest that a language-specific weighting of these cues develops during the first year of life; recent work on German revealed that eight-month-olds, unlike six-month-olds, are capable of perceiving a prosodic boundary on the basis of pitch change and final lengthening only. The present study uses Event-Related Potentials (ERPs) to investigate the neuro-cognitive development of prosodic cue perception in German-learning infants. In adults’ ERPs, prosodic boundary perception is clearly reflected by the so-called Closure Positive Shift (CPS). To date, there is mixed evidence on whether an infant CPS exists that signals early prosodic cue perception, or whether the CPS emerges only later—the latter implying that infantile brain responses to prosodic boundaries reflect acoustic, low-level pause detection. We presented six- and eight-month-olds with stimuli containing either no boundary cues, only a pitch cue, or a combination of both pitch change and final lengthening. For both age groups, responses to the former two conditions did not differ, while brain responses to prosodic boundaries cued by pitch change and final lengthening showed a positivity that we interpret as a CPS-like infant ERP component. This hints at an early sensitivity to prosodic boundaries that cannot exclusively be based on pause detection. Instead, infants’ brain responses indicate an early ability to exploit subtle, relational prosodic cues in speech perception—presumably even earlier than could be concluded from previous behavioral results.
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Raso, Tommaso, Bárbara Teixeira, and Plínio Barbosa. "Modelling automatic detection of prosodic boundaries for brazilian portuguese spontaneous speech." Journal of Speech Sciences 9 (September 9, 2020): 105–28. http://dx.doi.org/10.20396/joss.v9i00.14957.

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Speech is segmented into intonational units marked by prosodic boundaries. This segmentation is claimed to have important consequences on syntax, information structure and cognition. This work aims both to investigate the phonetic-acoustic parameters that guide the production and perception of prosodic boundaries, and to develop models for automatic detection of prosodic boundaries in male monological spontaneous speech of Brazilian Portuguese. Two samples were segmented into intonational units by two groups of trained annotators. The boundaries perceived by the annotators were tagged as either terminal or non-terminal. A script was used to extract 111 phonetic-acoustic parameters along speech signal in a right and left windows around the boundary of each phonological word. The extracted parameters comprise measures of (1) Speech rate and rhythm; (2) Standardized segment duration; (3) Fundamental frequency; (4) Intensity; (5) Silent pause. The script considers as prosodic boundary positions at which at least 50% of the annotators indicated a boundary of the same type. A training of models composed by the parameters extracted by the script was developed; these models, were then improved heuristically. The models were developed from the two samples and from the whole data, both using non-balanced and balanced data. Linear Discriminant Analysis algorithm was adopted to produce the models. The models for terminal boundaries show a much higher performance than those for non-terminal ones. In this paper we: (i) show the methodological procedures; (ii) analyze the different models; (iii) discuss some strategies that could lead to an improvement of our results.
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Mertens, Piet. "Polytonia." Journal of Speech Sciences 4, no. 2 (February 5, 2021): 17–57. http://dx.doi.org/10.20396/joss.v4i2.15053.

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This paper first proposes a labeling scheme for tonal aspects of speech and then describes an automatic annotation system using this transcription. This fine-grained transcription provides labels indicating pitch level and pitch movement of individual syllables. Of the five pitch levels, three (low, mid, high) are defined on the basis of pitch changes in the local context and two (bottom, top) are defined relative to the boundaries of the speaker’s global pitch range. For pitch movements, both simple and compound, the transcription indicates direction (rise, fall, level) and size, using size categories (pitch intervals) adjusted relative to the speaker’s pitch range. The automatic tonal annotation system combines several processing steps: segmentation into syllable peaks, pause detection, pitch stylization, pitch range estimation, classification of the intra-syllabic pitch contour, and pitch level assignment. It uses a dedicated and rule-based procedure, which unlike commonly used supervised learning techniques does not require a labeled corpus for training the model. The paper also includes a preliminary evaluation of the annotation system, for a reference corpus of nearly 14 minutes of spontaneous speech in French and Dutch, in order to quantify the annotation errors. The results, expressed in terms of standard measures of precision, recall, accuracy and Fmeasure are encouraging. For pitch levels low, mid and high an F-measure between 0.946 and 0.815 is obtained and for pitch movements a value between 0.708 and 1. Provided additional modules for the detection of prominence and prosodic boundaries, the resulting annotation may serve as an input for a phonological annotation.
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Biron, Tirza, Daniel Baum, Dominik Freche, Nadav Matalon, Netanel Ehrmann, Eyal Weinreb, David Biron, and Elisha Moses. "Automatic detection of prosodic boundaries in spontaneous speech." PLOS ONE 16, no. 5 (May 3, 2021): e0250969. http://dx.doi.org/10.1371/journal.pone.0250969.

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Automatic speech recognition (ASR) and natural language processing (NLP) are expected to benefit from an effective, simple, and reliable method to automatically parse conversational speech. The ability to parse conversational speech depends crucially on the ability to identify boundaries between prosodic phrases. This is done naturally by the human ear, yet has proved surprisingly difficult to achieve reliably and simply in an automatic manner. Efforts to date have focused on detecting phrase boundaries using a variety of linguistic and acoustic cues. We propose a method which does not require model training and utilizes two prosodic cues that are based on ASR output. Boundaries are identified using discontinuities in speech rate (pre-boundary lengthening and phrase-initial acceleration) and silent pauses. The resulting phrases preserve syntactic validity, exhibit pitch reset, and compare well with manual tagging of prosodic boundaries. Collectively, our findings support the notion of prosodic phrases that represent coherent patterns across textual and acoustic parameters.
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Dissertations / Theses on the topic "Speech pause detection"

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Podloucká, Lenka. "Identifikace pauz v rušeném řečovém signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217266.

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This diploma thesis deals with pause identification with degraded speech signal. The speech characteristics and the conception of speech signal processing are described here. The work aim was to create the reliable recognizing method to establish speech and non-speech segments of speech signal with and without degraded speech signal. The five empty pause detectors were realized in computing environment MATLAB. There was the energetic detector in time domain, two-step detector in spectral domain, one-step integral detector, two-step integral detector and differential detector in cepstrum. The spectral detector makes use of energetic characteristics of speech signal in first step and statistic analysis in second step. Cepstral detectors make use of integral or differential algorithms. The detectors robustness was tested for different types of speech degradation and different values of Signal to Noise Ratio. The test of influence different speech degradation was conducted to compare non-speech detection for detectors by ROC (Receiver Operating Characteristic) Curves.
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Gabriel, Karim, and Sani Al Moudarres. "The development of a Speech Level Adjustment Technique for late Deaf People." Thesis, Blekinge Tekniska Högskola, Avdelningen för signalbehandling, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4143.

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People that become deaf later on in life do have the ability to speak with correct pronunciation but since they can not hear their own voice nor the noise in the enviroment, they have difficulties to adjust their voice level to the surrounding environment. In this thesis we propose and algorithm which can be used on a prototype to help the late deafened people to adjust their voice level to the surrounding.
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Lee, Yi, and 李易. "A Preliminary Study on Automatic Detection of Filled Pause in Spontaneous Mandarin Speech." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/44822089669381457323.

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Marzinzik, Mark, and Mark Marzinzik@ePost de. "Noise Reduction Schemes for Digital Hearing Aids and their Use for the." 2000. http://www.bis.uni-oldenburg.de/dissertation/2001/marnoi00/marnoi00.html.

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Medeiros, Henrique Rodrigues Barbosa de. "Automatic detection of disfluencies in a corpus of university lectures." Master's thesis, 2014. http://hdl.handle.net/10071/8683.

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This dissertation focuses on the identification of disfluent sequences and their distinct structural regions. Reported experiments are based on audio segmentation and prosodic features, calculated from a corpus of university lectures in European Portuguese, containing about 32 hours of speech and about 7.7% of disfluencies. The set of features automatically extracted from the forced alignment corpus proved to be discriminant of the regions contained in the production of a disfluency. The best results concern the detection of the interregnum, followed by the detection of the interruption point. Several machine learning methods have been applied, but experiments show that Classification and Regression Trees usually outperform the other methods. The set of most informative features for cross-region identification encompasses word duration ratios, word confidence score, silent ratios, and pitch and energy slopes. Features such as the number of phones and syllables per word proved to be more useful for the identification of the interregnum, whereas energy slopes were most suited for identifying the interruption point. We have also conducted initial experiments on automatic detecting filled pauses, the most frequent disfluency type. For now, only force aligned transcripts were used, since the ASR system is not well adapted to this domain. This study is a step towards automatic detection of filled pauses for European Portuguese using prosodic features. Future work will extend this study for fully automatic transcripts, and will also tackle other domains, also exploring extended sets of linguistic features.
Esta tese aborda a identificação de sequências disfluentes e respetivas regiões estruturais. As experiências aqui descritas baseiam-se em segmentação e informação relativa a prosódia, calculadas a partir de um corpus de aulas universitárias em Português Europeu, contendo cerca de 32 horas de fala e de cerca de 7,7% de disfluências. O conjunto de características utilizadas provou ser discriminatório na identificação das regiões contidas na produção de disfluências. Os melhores resultados dizem respeito à deteção do interregnum, seguida da deteção do ponto de interrupção. Foram testados vários métodos de aprendizagem automática, sendo as Árvores de Decisão e Regressão as que geralmente obtiveram os melhores resultados. O conjunto de características mais informativas para a identificação e distinção de regiões disfluentes abrange rácios de duração de palavras, nível de confiança da palavra atual, rácios envolvendo silêncios e declives de pitch e de energia. Características tais como o número de fones e sílabas por palavra provaram ser mais úteis para a identificação do interregnum, enquanto pitch e energia foram os mais adequados para identificar o ponto de interrupção. Foram também realizadas experiências focando a deteção de pausas preenchidas. Por enquanto, para estas experiências foi utilizado apenas material proveniente de alinhamento forçado, já que o sistema de reconhecimento automático não está bem adaptado a este domínio. Este estudo representa um novo passo no sentido da deteção automática de pausas preenchidas para Português Europeu, utilizando recursos prosódicos. Em trabalho futuro pretende-se estender esse estudo para transcrições automáticas e também abordar outros domínios, explorando conjuntos mais extensos de características linguísticas.
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Book chapters on the topic "Speech pause detection"

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Sani, Auliya, Dessi Puji Lestari, and Ayu Purwarianti. "Filled Pause Detection in Indonesian Spontaneous Speech." In Communications in Computer and Information Science, 54–64. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0515-2_4.

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Yang, Zhanlei, Wenju Liu, Wei Jiang, Pengfei Hu, and Mingming Chen. "Speech Fragment Decoding Techniques Using Silent Pause Detection." In Communications in Computer and Information Science, 579–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33506-8_71.

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Hamzah, Raseeda, Nursuriati Jamil, and Noraini Seman. "Nurturing Filled Pause Detection for Spontaneous Speech Retrieval." In Information Retrieval Technology, 458–69. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12844-3_39.

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Verkhodanova, Vasilisa, and Vladimir Shapranov. "Detecting Filled Pauses and Lengthenings in Russian Spontaneous Speech Using SVM." In Speech and Computer, 224–31. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43958-7_26.

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Verkhodanova, Vasilisa, and Vladimir Shapranov. "Multi-factor Method for Detection of Filled Pauses and Lengthenings in Russian Spontaneous Speech." In Speech and Computer, 285–92. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23132-7_35.

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Verkhodanova, Vasilisa, and Vladimir Shapranov. "Filled Pauses and Lengthenings Detection Based on the Acoustic Features for the Spontaneous Russian Speech." In Speech and Computer, 227–34. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11581-8_28.

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Conference papers on the topic "Speech pause detection"

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Stejskal, Vojtech, Nikolaos Bourbakis, and Anna Esposito. "Empty Speech Pause Detection in Spontaneous Speech." In 2009 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2009. http://dx.doi.org/10.1109/ictai.2009.90.

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Liu, Zhenyu, Huanyu Kang, Lei Feng, and Lan Zhang. "Speech pause time: A potential biomarker for depression detection." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217971.

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Li, Yan-Xiong, Qian-Hua He, and Tao Li. "A Novel Detection Method of Filled Pause in Mandarin Spontaneous Speech." In Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008). IEEE, 2008. http://dx.doi.org/10.1109/icis.2008.26.

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Elyasi Langarani, Mahsa Sadat, and Jan van Santen. "Automatic, model-based detection of pause-less phrase boundaries from fundamental frequency and duration features." In 9th ISCA Speech Synthesis Workshop. ISCA, 2016. http://dx.doi.org/10.21437/ssw.2016-1.

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Audhkhasi, Kartik, Kundan Kandhway, Om D. Deshmukh, and Ashish Verma. "Formant-based technique for automatic filled-pause detection in spontaneous spoken english." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960719.

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Alimuradov, Alan K., Alexander Yu Tychkov, Alexey V. Ageykin, Pyotr P. Churakov, Yury S. Kvitka, and Alexey P. Zaretskiy. "Speech/pause detection algorithm based on the adaptive method of complementary decomposition and energy assessment of intrinsic mode functions." In 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM). IEEE, 2017. http://dx.doi.org/10.1109/scm.2017.7970665.

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Shimp, Samuel K., Steve C. Southward, and Mehdi Ahmadian. "Detecting Crew Alertness With Processed Speech." In ASME/IEEE 2007 Joint Rail Conference and Internal Combustion Engine Division Spring Technical Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/jrc/ice2007-40101.

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This paper proposes a solution for improving the safety of rail and other mass transportation systems through operator alertness monitoring. A non-invasive method of alertness monitoring through speech processing is presented. Speech analysis identifies measurable vocal tract changes due to fatigue and decreased speech rate due to decreased mental ability. Enabled by existing noise reduction technology, a system has been designed for measuring key speech features that are believed to correlate to alertness level. The features of interest are pitch, word intensity, pauses between words and phrases, and word rate. The purpose of this paper is to describe the overall alertness monitoring system design and then to show some experimental results for the core processing algorithm which extracts features from the speech. The feature extraction algorithm proposed here uses a new and simple technique to parse the continuous speech signal coming from the communication signal without using computationally demanding and error-prone word recognition techniques. Preliminary results on the core feature extraction algorithm indicate that words, phrases, and rates can be determined for relatively noise-free speech signals. Once the remainder of the overall alertness monitoring system is complete, it will be applied to real life recordings of train operators and will be subjected to clinical testing to determine alert and non-alert levels of the speech features of interest.
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Wang, Quanyi, and Jinping Li. "Filled Pause of Spontaneous Speech Detecting based on Multi-Features." In 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP). IEEE, 2020. http://dx.doi.org/10.1109/icsip49896.2020.9339261.

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Verkhodanova, Vasilisa, and Vladimir Shapranov. "Automatic Detection of Filled Pauses and Lengthenings in the Spontaneous Russian Speech." In 7th International Conference on Speech Prosody 2014. ISCA: ISCA, 2014. http://dx.doi.org/10.21437/speechprosody.2014-211.

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Prylipko, Dmytro, Olga Egorow, Ingo Siegert, and Andreas Wendemuth. "Application of image processing methods to filled pauses detection from spontaneous speech." In Interspeech 2014. ISCA: ISCA, 2014. http://dx.doi.org/10.21437/interspeech.2014-413.

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