Literature DB >> 33003882

Improving hearing-aid gains based on automatic speech recognition.

Lionel Fontan1, Maxime Le Coz1, Charlotte Azzopardi2, Michael A Stone3, Christian Füllgrabe4.   

Abstract

This study provides proof of concept that automatic speech recognition (ASR) can be used to improve hearing aid (HA) fitting. A signal-processing chain consisting of a HA simulator, a hearing-loss simulator, and an ASR system normalizing the intensity of input signals was used to find HA-gain functions yielding the highest ASR intelligibility scores for individual audiometric profiles of 24 listeners with age-related hearing loss. Significantly higher aided speech intelligibility scores and subjective ratings of speech pleasantness were observed when the participants were fitted with ASR-established gains than when fitted with the gains recommended by the CAM2 fitting rule.

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Year:  2020        PMID: 33003882     DOI: 10.1121/10.0001866

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  2 in total

1.  OPRA-RS: A Hearing-Aid Fitting Method Based on Automatic Speech Recognition and Random Search.

Authors:  Libio Gonçalves Braz; Lionel Fontan; Julien Pinquier; Michael A Stone; Christian Füllgrabe
Journal:  Front Neurosci       Date:  2022-02-21       Impact factor: 4.677

2.  Using Automatic Speech Recognition to Optimize Hearing-Aid Time Constants.

Authors:  Lionel Fontan; Libio Gonçalves Braz; Julien Pinquier; Michael A Stone; Christian Füllgrabe
Journal:  Front Neurosci       Date:  2022-03-17       Impact factor: 4.677

  2 in total

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