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Predicting speech perception in older listeners with sensorineural hearing loss using automatic speech recognition
journal contribution
posted on 2021-01-22, 16:27 authored by Lionel Fontan, Tom Cretin-Maitenaz, Christian FullgrabeThe 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.
Funding
Institute of Advanced Studies of Loughborough University (UK)
History
School
- Sport, Exercise and Health Sciences
Published in
Trends in HearingVolume
24Pages
1 - 16Publisher
SAGE PublicationsVersion
- VoR (Version of Record)
Rights holder
© The AuthorsPublisher statement
This is an Open Access Article. It is published by SAGE under the Creative Commons Attribution-NonCommercial 4.0 International Licence (CC BY-NC 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by-nc/4.0/Acceptance date
2020-03-02Publication date
2020-04-01Copyright date
2020ISSN
2331-2165eISSN
2331-2165Publisher version
Language
- en
Depositor
Dr Christian Fullgrabe. Deposit date: 20 January 2021Usage metrics
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