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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2020
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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. |
format | Online Article Text |
id | pubmed-7119229 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
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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