Fontan Lionel, Le Coz Maxime, Azzopardi Charlotte, Stone Michael A, Füllgrabe Christian
Archean LABS, 20 place Prax-Paris, 82000 Montauban, France.
Ecole d'Audioprothèse de Cahors, Université Toulouse III Paul Sabatier, 31062 Toulouse, France.
J Acoust Soc Am. 2020 Sep;148(3):EL227. doi: 10.1121/10.0001866.
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.
本研究提供了概念验证,即自动语音识别(ASR)可用于改善助听器(HA)验配。使用由HA模拟器、听力损失模拟器和对输入信号强度进行归一化的ASR系统组成的信号处理链,来寻找针对24名患有年龄相关性听力损失的听众的个体听力图,能产生最高ASR可懂度分数的HA增益函数。当参与者佩戴根据ASR确定的增益进行验配时,观察到的助听语音可懂度分数和语音愉悦度主观评分显著高于根据CAM2验配规则推荐的增益进行验配时的情况。