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Relevant bibliographies by topics / Speech intelligibility prediction

Academic literature on the topic 'Speech intelligibility prediction'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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Journal articles on the topic "Speech intelligibility prediction"

1

Bradley, J. S. "Speech intelligibility prediction in rooms." Journal of the Acoustical Society of America 77, S1 (April 1985): S88. http://dx.doi.org/10.1121/1.2022570.

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Tamjis, Mohd Ridzwan, Muhammad Naufal Mansor, Ahmad Kadri Junoh, Amran Ahmed, Wan Suhana Wan Daud, and Azrini Idris. "Heterogeneous Speech Prediction Using LDA Classifiers." Advanced Materials Research 1016 (August 2014): 267–72. http://dx.doi.org/10.4028/www.scientific.net/amr.1016.267.

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Classroom speech intelligibility has become one of the main concerns in schools and other learning institutions development nowadays. This is because the qualities of student’s perceptions towards teacher are essentials in learning development. Measures have been introduced by the acoustical association to tackle the speech intelligibility problems in the classroom such as room renovations. Room’s acoustics standards have been introduced in several countries but still the questions on whether the standards fits on every classroom in different countries are still arise. Studies have also shown that most of the researches that have been conducted were only focusing on the conventional type classroom which depends only on the teacher’s vocal power. This paper will formulate the measurement protocol on measuring the speech intelligibility in the sound reinforced (multiple speaker) classroom. Finally it was found that the speech intelligibility in the sound reinforced classroom is better than the conventional classroom by using Linear Discriminant Analysis.

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van Schoonhoven, Jelmer, Koenraad S. Rhebergen, and Wouter A. Dreschler. "A context-based approach to predict speech intelligibility in interrupted noise: Model design." Journal of the Acoustical Society of America 151, no. 2 (February 2022): 1404–15. http://dx.doi.org/10.1121/10.0009617.

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The Extended Speech Transmission Index (ESTI) by van Schoonhoven et al. [(2019). J. Acoust. Soc. Am. 145, 1178–1194] was used successfully to predict intelligibility of sentences in fluctuating background noise. However, prediction accuracy was poor when the modulation frequency of the masker was low (<8 Hz). In the current paper, the ESTI was calculated per phoneme to estimate phoneme intelligibility. In the next step, the ESTI model was combined with one of two context models {Boothroyd and Nittrouer, [(1988). J. Acoust. Soc. Am. 84, 101–114]; Bronkhorst et al., [(1993). J. Acoust. Soc. Am. 93, 499–509} in order to improve model predictions. This approach was validated using interrupted speech data, after which it was used to predict speech intelligibility of words in interrupted noise. Model predictions improved using this new method, especially for maskers with interruption rates below 5 Hz. Calculating the ESTI at phoneme level combined with a context model is therefore a viable option to improve prediction accuracy.

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Marshall, L. Gerald. "Speech intelligibility prediction from calculated C50 values." Journal of the Acoustical Society of America 98, no. 5 (November 1995): 2845–47. http://dx.doi.org/10.1121/1.413184.

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Jensen, Jesper, and Cees H. Taal. "Speech Intelligibility Prediction Based on Mutual Information." IEEE/ACM Transactions on Audio, Speech, and Language Processing 22, no. 2 (February 2014): 430–40. http://dx.doi.org/10.1109/taslp.2013.2295914.

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Yamamoto, Katsuhiko, Toshio Irino, Toshie Matsui, Shoko Araki, Keisuke Kinoshita, and Tomohiro Nakatani. "Analysis of acoustic features for speech intelligibility prediction models analysis of acoustic features for speech intelligibility prediction models." Journal of the Acoustical Society of America 140, no. 4 (October 2016): 3114. http://dx.doi.org/10.1121/1.4969744.

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Croce, Paolo, Francesco Leccese, Giacomo Salvadori, and Umberto Berardi. "Proposal of a Simplified Tool for Early Acoustics Design Stage of Classrooms in Compliance with Speech Intelligibility Thresholds." Energies 16, no. 2 (January 10, 2023): 813. http://dx.doi.org/10.3390/en16020813.

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The speech intelligibility properties of classrooms greatly influence the learning process of students. Proper acoustics can promote the inclusion of foreign students and children with learning or hearing impairments. While awareness of the topic is increasing, there is still no parameter that can describe all aspects of speech transmission inside a room. This complicates the design of classrooms and requires designers to have extensive knowledge of theory and experience. In the scientific and technical literature, there is a lack of predictive tools, easy to use by designers, which can guide the choices in the early design stages in order to move towards technical solutions able to ensure adequate levels of speech intelligibility. For this reason, in this paper, the most relevant speech intelligibility parameters found in the literature were collected and discussed. Among these, the Clarity index and Speech Transmission Index were singled out as the most effective ones, whose prediction can be made with relatively simple methods. They were then analyzed through their prediction formulas, and a tool was proposed to allow an easy estimation of the minimum total equivalent sound absorption area needed in a classroom. This tool greatly simplifies the early acoustics design stage, allowing the intelligibility of speech within a classroom to be increased without requiring much theoretical effort on the part of the designers.

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Bernstein, Joshua G. W., Van Summers, Elena Grassi, and Ken W. Grant. "Auditory Models of Suprathreshold Distortion and Speech Intelligibility in Persons with Impaired Hearing." Journal of the American Academy of Audiology 24, no. 04 (April 2013): 307–28. http://dx.doi.org/10.3766/jaaa.24.4.6.

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Background: Hearing-impaired (HI) individuals with similar ages and audiograms often demonstrate substantial differences in speech-reception performance in noise. Traditional models of speech intelligibility focus primarily on average performance for a given audiogram, failing to account for differences between listeners with similar audiograms. Improved prediction accuracy might be achieved by simulating differences in the distortion that speech may undergo when processed through an impaired ear. Although some attempts to model particular suprathreshold distortions can explain general speech-reception deficits not accounted for by audibility limitations, little has been done to model suprathreshold distortion and predict speech-reception performance for individual HI listeners. Auditory-processing models incorporating individualized measures of auditory distortion, along with audiometric thresholds, could provide a more complete understanding of speech-reception deficits by HI individuals. A computational model capable of predicting individual differences in speech-recognition performance would be a valuable tool in the development and evaluation of hearing-aid signal-processing algorithms for enhancing speech intelligibility. Purpose: This study investigated whether biologically inspired models simulating peripheral auditory processing for individual HI listeners produce more accurate predictions of speech-recognition performance than audiogram-based models. Research Design: Psychophysical data on spectral and temporal acuity were incorporated into individualized auditory-processing models consisting of three stages: a peripheral stage, customized to reflect individual audiograms and spectral and temporal acuity; a cortical stage, which extracts spectral and temporal modulations relevant to speech; and an evaluation stage, which predicts speech-recognition performance by comparing the modulation content of clean and noisy speech. To investigate the impact of different aspects of peripheral processing on speech predictions, individualized details (absolute thresholds, frequency selectivity, spectrotemporal modulation [STM] sensitivity, compression) were incorporated progressively, culminating in a model simulating level-dependent spectral resolution and dynamic-range compression. Study Sample: Psychophysical and speech-reception data from 11 HI and six normal-hearing listeners were used to develop the models. Data Collection and Analysis: Eleven individualized HI models were constructed and validated against psychophysical measures of threshold, frequency resolution, compression, and STM sensitivity. Speech-intelligibility predictions were compared with measured performance in stationary speech-shaped noise at signal-to-noise ratios (SNRs) of −6, −3, 0, and 3 dB. Prediction accuracy for the individualized HI models was compared to the traditional audibility-based Speech Intelligibility Index (SII). Results: Models incorporating individualized measures of STM sensitivity yielded significantly more accurate within-SNR predictions than the SII. Additional individualized characteristics (frequency selectivity, compression) improved the predictions only marginally. A nonlinear model including individualized level-dependent cochlear-filter bandwidths, dynamic-range compression, and STM sensitivity predicted performance more accurately than the SII but was no more accurate than a simpler linear model. Predictions of speech-recognition performance simultaneously across SNRs and individuals were also significantly better for some of the auditory-processing models than for the SII. Conclusions: A computational model simulating individualized suprathreshold auditory-processing abilities produced more accurate speech-intelligibility predictions than the audibility-based SII. Most of this advantage was realized by a linear model incorporating audiometric and STM-sensitivity information. Although more consistent with known physiological aspects of auditory processing, modeling level-dependent changes in frequency selectivity and gain did not result in more accurate predictions of speech-reception performance.

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Jin, In-Ki, James M. Kates, and Kathryn H. Arehart. "Sensitivity of the Speech Intelligibility Index to the Assumed Dynamic Range." Journal of Speech, Language, and Hearing Research 60, no. 6 (June 10, 2017): 1674–80. http://dx.doi.org/10.1044/2017_jslhr-h-16-0348.

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Purpose This study aims to evaluate the sensitivity of the speech intelligibility index (SII) to the assumed speech dynamic range (DR) in different languages and with different types of stimuli. Method Intelligibility prediction uses the absolute transfer function (ATF) to map the SII value to the predicted intelligibility for a given stimuli. To evaluate the sensitivity of the predicted intelligibility to the assumed DR, ATF-transformed SII scores for English (words), Korean (sentences), and Mandarin (sentences) were derived for DRs ranging from 10 dB to 60 dB. Results Increasing the assumed DR caused steeper ATFs for all languages. However, high correlation coefficients between predicted and measured intelligibility scores were observed for DRs from 20 dB to 60 dB for ATFs in English, Korean, and Mandarin. Conclusions Results of the present study indicate that the intelligibility computed from the SII is not sensitive to the assumed DR. The 30-dB DR commonly used in computing the SII is thus a reasonable assumption that produces accurate predictions for different languages and different types of stimuli.

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Kim, Gwang Min, and Jae Hee Lee. "Prediction of Software-Based Sentence-in-Noise Recognition Thresholds of Hearing-Impaired Listeners." Audiology and Speech Research 16, no. 2 (April 30, 2020): 140–46. http://dx.doi.org/10.21848/asr.200015.

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Purpose: Although hearing-impaired (HI) listeners often have difficulty understanding in noise as their primary complaints, the speech-in-noise intelligibility test is not conducted as a standard audiologic test battery. This study investigated whether the speech audiometry in quiet accurately reflects the sentence-in-noise intelligibility of HI listeners. Methods: Sixty-two HI listeners participated. All the HI listeners had symmetrical high-frequency hearing loss and bilaterally worn hearing aids. Twenty-five normal-hearing (NH) listeners also participated as a control group. The unaided word and sentence recognition scores (WRS and SRS) were obtained in quiet at individually determined most comfortable loudness level. With bilateral hearing aids, the aided WRS and SRS were evaluated at a normal conversational level. The software-based Korean Matrix sentence in noise test was administered at a fixed level (65 dB SPL) of noise while adjusting the sentence level adaptively based on the listener’s response. The signal-to-noise ratio (SNR) required to achieve 50% intelligibility (speech recognition thresholds, SRTs) was obtained. Results: On average, the aided SRT of HI listeners was 0.1 dB SNR, and the mean SRT of NH adults was -8.91 dB SNR. The Matrix sentence-in-noise intelligibility was not sufficiently explained by the unaided WRS or unaided SRS. Conclusion: A traditional measure of the unaided speech-in-quiet recognition cannot accurately predict the aided speech-innoise intelligibility. Clinically, a software-based sentence-in-noise intelligibility test is recommended to directly confirm the actual benefits of hearing aid in noisy situations.

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Dissertations / Theses on the topic "Speech intelligibility prediction"

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Leopold, Sarah Yoho. "Factors Influencing the Prediction of Speech Intelligibility." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1460464847.

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Ellaham, Nicolas. "Binaural Speech Intelligibility Prediction and Nonlinear Hearing Devices." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31713.

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A new objective measurement system to predict speech intelligibility in binaural listening conditions is proposed for use with nonlinear hearing devices. Digital processing inside such devices often involves nonlinear operations such as clipping, compression, and noise reduction algorithms. Standard objective measures such as the Articulation Indeix (AI), the Speech Intelligibility Index (SII) and the Speech Transmission Index (STI) have been developed for monaural listening. Binaural extensions of these measures have been proposed in the literature, essentially consisting of a binaural pre-processing stage followed by monaural intelligibility prediction using the better ear or the binaurally enhanced signal. In this work, a three-stage extension of the binaural SII approach is proposed that deals with nonlinear acoustic input signals. The reference-based model operates as follows: (1) a stage to deal with nonlinear processing based on a signal-separation model to recover estimates of speech, noise and distortion signals at the output of hearing devices; (2) a binaural processing stage using the Equalization-Cancellation (EC) model; and (3) a stage for intelligibility prediction using the SII or the short-time Extended SII (ESII). Multiple versions of the model have been developed and tested for use with hearing devices. A software simulator is used to perform hearing-device processing under various binaural listening conditions. Details of the modeling procedure are discussed along with an experimental framework for collecting subjective intelligibility data. In the absence of hearing-device processing, the model successfully predicts speech intelligibility in all spatial configurations considered. Varying levels of success were obtained using two simple distortion modeling approaches with different distortion mechanisms. Future refinements to the model are proposed based on the results discussed in this work.

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Taghia, Jalal [Verfasser], Rainer [Gutachter] Martin, and Richard C. [Gutachter] Hendriks. "Speech intelligibility prediction and single-channel noise reduction based on information measures / Jalal Taghia ; Gutachter: Rainer Martin, Richard C. Hendriks." Bochum : Ruhr-Universität Bochum, 2016. http://d-nb.info/111944733X/34.

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Leclère, Thibaud. "Towards a binaural model for predicting speech intelligibility among competing voices in rooms." Thesis, Vaulx-en-Velin, Ecole nationale des travaux publics, 2015. http://www.theses.fr/2015ENTP0008/document.

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Ce travail de thèse vise à proposer un modèle pouvant prédire l’intelligibilité d’une voix cible masquée par des sources concurrentes dans les salles. Un modèle a déjà été développé par Lavandier et Culling (2010) et est capable de prédire l’intelligibilité d’une cible en champ proche perturbée par plusieurs sources de bruit. Le travail présenté ici traite des nouvelles implémentations et expérimentations nécessaires pour étendre le modèle au cas de cibles distantes et au cas de voix concurrentes, qui présentent des propriétés acoustiques différentes des bruits stationnaires (fluctuation d’enveloppe, fréquence fondamentale, modulations de fréquence fondamentale). L’effet nuisible de la réverbération sur la parole cible a été implémenté avec succès. Cette nouvelle version du modèle permet une interprétation unifiée de plusieurs effetsperceptifs observés dans la littérature mais il présente une dépendance de la salle, ce qui limite son aspect prédictif. Des travaux expérimentaux ont été menés pour déterminer comment le modèle pourrait prendre en compte le cas de sources cibles et masquantes avec des spectres différents ainsi que le cas où plusieurs mécanismes auditifs opèrent simultanément (ségrégation par F0, démasquage spatialet écoute dans les creux de modulation)
This PhD work aims to propose a model predicting the perceived intelligibility of a target speech masked by competing sources in rooms. An existing model developed by Lavandier and Culling (2010) is already able to predict speech intelligibility of a near-field target in the presence of multiple noise sources. The present work deals with new implementations and experimental work needed to extend the model tothe case of a distant target and to the case of masking voices, which present different acoustical properties than noises (envelope fluctuations, fundamental frequency, modulations of fundamental frequency). The detrimental effect of reverberation on the target speech has been successfully implemented. This new version of the model provides a unified interpretation of several perceptual effects previously observed in the literature but it presents a room dependency which limits its predictive power. Experimental work has been conducted to determine how the model could account for sources presenting different spectra, and to account for several auditory mechanisms operating simultaneously (F0 segregation, spatial unmasking and temporal dip listening)

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Messing, David P. (David Patrick) 1979. "Predicting confusions and intelligibility of noisy speech." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42246.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (leaves 202-207).
Current predictors of speech intelligibility are inadequate for making predictions of speech confusions caused by acoustic interference. This thesis is inspired by the need for a capability to understand and predict speech confusions caused by acoustic interference. The goal of this thesis is to develop models of auditory speech processing capable of predicting phonetic confusions by normally-hearing listeners, under a variety of acoustic distortions. In particular, we focus on modeling the Medial Olivocochlear efferent pathway (which provides feedback from the brain stem to the peripheral auditory system) and demonstrate its potential for speech identification in noise. Our results produced representations and performance that were robust to varying levels of additive noise and which mimicked human performance as measured by the Chi-squared test.
by David P. Messing.
Ph.D.

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Book chapters on the topic "Speech intelligibility prediction"

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Tognola, Gabriella, Stefano Moriconi, and Emma Chiaramello. "On the Use of Acoustic Simulations and PESQ Measures for the Prediction of Speech Intelligibility in Sensorineural Hearing Loss." In IFMBE Proceedings, 9–12. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11128-5_3.

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Culling, J. F., M. Lavandier, and S. Jelfs. "Predicting Binaural Speech Intelligibility in Architectural Acoustics." In The Technology of Binaural Listening, 427–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37762-4_16.

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Holube, Inga, Matthias Wesselkamp, Wouter A. Dreschler, and Birger Kollmeier. "Speech Intelligibility Prediction in Hearing-Impaired Listeners for Steady and Fluctuating Noise." In Modeling Sensorineural Hearing Loss, 447–59. Routledge, 2019. http://dx.doi.org/10.4324/9781315789392-35.

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Nijs, L., and A. Schuur. "Expressing legal demands in acoustical quantities; is the reverberation time a good predictor for the speech intelligibility in a sports hall?" In Research in Building Physics, 879–87. CRC Press, 2020. http://dx.doi.org/10.1201/9781003078852-123.

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Conference papers on the topic "Speech intelligibility prediction"

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Alghamdi, Ahmed, and Wai-Yip Chan. "Modified ESTOI for improving speech intelligibility prediction." In 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2020. http://dx.doi.org/10.1109/ccece47787.2020.9255677.

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Andersen, Asger Heidemann, Esther Schoenmaker, and Steven van de Par. "Speech intelligibility prediction as a classification problem." In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2016. http://dx.doi.org/10.1109/mlsp.2016.7738814.

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Karbasi, Mahdie, Ahmed Hussen Abdelaziz, and Dorothea Kolossa. "Twin-HMM-based non-intrusive speech intelligibility prediction." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7471750.

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Sørensen, Charlotte, Jesper B. Boldt, and Mads G. Christensen. "Harmonic Beamformers for Non-Intrusive Speech Intelligibility Prediction." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2929.

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Paulraj, M. P., S. Yaacob, A. N. Abdullah, M. Thagirarani, and M. R. Tamjis. "Classroom speech intelligibility prediction using Elman neural network." In its Applications (CSPA). IEEE, 2010. http://dx.doi.org/10.1109/cspa.2010.5545255.

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Ahnert, Wolfgang, Stefan Feistel, and Tobias Behrens. "Speech intelligibility prediction in very large sacral venues." In ICA 2013 Montreal. ASA, 2013. http://dx.doi.org/10.1121/1.4801009.

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Tamjis, M. Ridhwan, Sazali Yaacob, Paul Raj M. Pandian, A. Nazri Abdullah, and Raymond Boon Whee Heng. "Feature based classification for classroom speech intelligibility prediction." In 2011 National Postgraduate Conference (NPC). IEEE, 2011. http://dx.doi.org/10.1109/natpc.2011.6136318.

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Sorensen, Charlotte, Angeliki Xenaki, Jesper B. Boldt, and Mads G. Christensen. "Pitch-based non-intrusive objective intelligibility prediction." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952183.

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Chen, Fei, and Tian Guan. "Non-intrusive intelligibility prediction for Mandarin speech in noise." In TENCON 2013 - 2013 IEEE Region 10 Conference. IEEE, 2013. http://dx.doi.org/10.1109/tencon.2013.6719062.

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Karbasi, Mahdie, Ahmed Hussen Abdelaziz, Hendrik Meutzner, and Dorothea Kolossa. "Blind Non-Intrusive Speech Intelligibility Prediction Using Twin-HMMs." In Interspeech 2016. ISCA, 2016. http://dx.doi.org/10.21437/interspeech.2016-155.

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Reports on the topic "Speech intelligibility prediction"

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Scharine, Angelique A., Paula P. Henry, Mohan D. Rao, and Jason T. Dreyer. A Model for Predicting Intelligibility of Binaurally Perceived Speech. Fort Belvoir, VA: Defense Technical Information Center, April 2007. http://dx.doi.org/10.21236/ada466840.

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