Fontan, Lionel;
Gonçalves Braz, Libio;
Pinquier, Julien;
Stone, Michael A;
Füllgrabe, Christian;
(2022)
Using Automatic Speech Recognition to Optimize Hearing-Aid Time Constants.
Frontiers in Neuroscience
, 16
, Article 779062. 10.3389/fnins.2022.779062.
Preview |
Text
Fontan_etal_FINS22.pdf - Other Download (2MB) | Preview |
Abstract
Automatic speech recognition (ASR), when combined with hearing-aid (HA) and hearing-loss (HL) simulations, can predict aided speech-identification performances of persons with age-related hearing loss. ASR can thus be used to evaluate different HA configurations, such as combinations of insertion-gain functions and compression thresholds, in order to optimize HA fitting for a given person. The present study investigated whether, after fixing compression thresholds and insertion gains, a random-search algorithm could be used to optimize time constants (i.e., attack and release times) for 12 audiometric profiles. The insertion gains were either those recommended by the CAM2 prescription rule or those optimized using ASR, while compression thresholds were always optimized using ASR. For each audiometric profile, the random-search algorithm was used to vary time constants with the aim to maximize ASR performance. A HA simulator and a HL simulator simulator were used, respectively, to amplify and to degrade speech stimuli according to the input audiogram. The resulting speech signals were fed to an ASR system for recognition. For each audiogram, 1,000 iterations of the random-search algorithm were used to find the time-constant configuration yielding the highest ASR score. To assess the reproducibility of the results, the random search algorithm was run twice. Optimizing the time constants significantly improved the ASR scores when CAM2 insertion gains were used, but not when using ASR-based gains. Repeating the random search yielded similar ASR scores, but different time-constant configurations.
| Type: | Article |
|---|---|
| Title: | Using Automatic Speech Recognition to Optimize Hearing-Aid Time Constants |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fnins.2022.779062 |
| Publisher version: | http://dx.doi.org/10.3389/fnins.2022.779062 |
| Language: | English |
| Additional information: | Copyright © 2022 Fontan, Gonçalves Braz, Pinquier, Stone and Füllgrabe. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | random search, automatic speech recognition, hearing aids, age-related hearing loss, compression speed, attack time, release time |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > The Ear Institute UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10155400 |
Archive Staff Only
 |
View Item |
