Individual speech recognition in noise, the audiogram and more: Using automatic speech recognition (ASR) as a modelling tool
To characterize the individual patient’s hearing impairment, a framework for auditory discrimination experiments (FADE, Schädler et al., 2015) was extended here using different degrees of individualization. FADE has been shown to predict the outcome of both speech recognition tests and psychoacoustic experiments based on simulations using an automatic speech recognition (ASR) system which requires only few assumptions. It builds on the closed-set matrix sentence recognition test which is advantageous for testing individual speech recognition in a way comparable across languages. Individual predictions of speech recognition thresholds in stationary and in fluctuating noise were derived using the audiogram and an estimate of the internal detector noise (“level uncertainty”). Either “typical” audiogram shapes with or without a “typical” level uncertainty or the individual data were used for individual predictions. As a result, the individualisation of the level uncertainty was found to be more important than the exact shape of the individual audiogram to accurately model the outcome of the German matrix test in stationary or fluctuating noise for listeners with hearing impairment.
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