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Predicting Speech Perception in Older Listeners with Sensorineural Hearing Loss Using Automatic Speech Recognition

The objective of this study was to provide proof of concept that the speech intelligibility in quiet of unaided older hearing-impaired (OHI) listeners can be predicted by automatic speech recognition (ASR). Twenty-four OHI listeners completed three speech-identification tasks using speech materials...

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Autores principales: Fontan, Lionel, Cretin-Maitenaz, Tom, Füllgrabe, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7119229/
https://www.ncbi.nlm.nih.gov/pubmed/32233834
http://dx.doi.org/10.1177/2331216520914769
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author Fontan, Lionel
Cretin-Maitenaz, Tom
Füllgrabe, Christian
author_facet Fontan, Lionel
Cretin-Maitenaz, Tom
Füllgrabe, Christian
author_sort Fontan, Lionel
collection PubMed
description The objective of this study was to provide proof of concept that the speech intelligibility in quiet of unaided older hearing-impaired (OHI) listeners can be predicted by automatic speech recognition (ASR). Twenty-four OHI listeners completed three speech-identification tasks using speech materials of varying linguistic complexity and predictability (i.e., logatoms, words, and sentences). An ASR system was first trained on different speech materials and then used to recognize the same speech stimuli presented to the listeners but processed to mimic some of the perceptual consequences of age-related hearing loss experienced by each of the listeners: the elevation of hearing thresholds (by linear filtering), the loss of frequency selectivity (by spectrally smearing), and loudness recruitment (by raising the amplitude envelope to a power). Independently of the size of the lexicon used in the ASR system, strong to very strong correlations were observed between human and machine intelligibility scores. However, large root-mean-square errors (RMSEs) were observed for all conditions. The simulation of frequency selectivity loss had a negative impact on the strength of the correlation and the RMSE. Highest correlations and smallest RMSEs were found for logatoms, suggesting that the prediction system reflects mostly the functioning of the peripheral part of the auditory system. In the case of sentences, the prediction of human intelligibility was significantly improved by taking into account cognitive performance. This study demonstrates for the first time that ASR, even when trained on intact independent speech material, can be used to estimate trends in speech intelligibility of OHI listeners.
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spelling pubmed-71192292020-04-13 Predicting Speech Perception in Older Listeners with Sensorineural Hearing Loss Using Automatic Speech Recognition Fontan, Lionel Cretin-Maitenaz, Tom Füllgrabe, Christian Trends Hear Original Article The objective of this study was to provide proof of concept that the speech intelligibility in quiet of unaided older hearing-impaired (OHI) listeners can be predicted by automatic speech recognition (ASR). Twenty-four OHI listeners completed three speech-identification tasks using speech materials of varying linguistic complexity and predictability (i.e., logatoms, words, and sentences). An ASR system was first trained on different speech materials and then used to recognize the same speech stimuli presented to the listeners but processed to mimic some of the perceptual consequences of age-related hearing loss experienced by each of the listeners: the elevation of hearing thresholds (by linear filtering), the loss of frequency selectivity (by spectrally smearing), and loudness recruitment (by raising the amplitude envelope to a power). Independently of the size of the lexicon used in the ASR system, strong to very strong correlations were observed between human and machine intelligibility scores. However, large root-mean-square errors (RMSEs) were observed for all conditions. The simulation of frequency selectivity loss had a negative impact on the strength of the correlation and the RMSE. Highest correlations and smallest RMSEs were found for logatoms, suggesting that the prediction system reflects mostly the functioning of the peripheral part of the auditory system. In the case of sentences, the prediction of human intelligibility was significantly improved by taking into account cognitive performance. This study demonstrates for the first time that ASR, even when trained on intact independent speech material, can be used to estimate trends in speech intelligibility of OHI listeners. SAGE Publications 2020-04-01 /pmc/articles/PMC7119229/ /pubmed/32233834 http://dx.doi.org/10.1177/2331216520914769 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Article
Fontan, Lionel
Cretin-Maitenaz, Tom
Füllgrabe, Christian
Predicting Speech Perception in Older Listeners with Sensorineural Hearing Loss Using Automatic Speech Recognition
title Predicting Speech Perception in Older Listeners with Sensorineural Hearing Loss Using Automatic Speech Recognition
title_full Predicting Speech Perception in Older Listeners with Sensorineural Hearing Loss Using Automatic Speech Recognition
title_fullStr Predicting Speech Perception in Older Listeners with Sensorineural Hearing Loss Using Automatic Speech Recognition
title_full_unstemmed Predicting Speech Perception in Older Listeners with Sensorineural Hearing Loss Using Automatic Speech Recognition
title_short Predicting Speech Perception in Older Listeners with Sensorineural Hearing Loss Using Automatic Speech Recognition
title_sort predicting speech perception in older listeners with sensorineural hearing loss using automatic speech recognition
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7119229/
https://www.ncbi.nlm.nih.gov/pubmed/32233834
http://dx.doi.org/10.1177/2331216520914769
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