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.
Highlights
Hearing-aid (HA) fitting is typically a two-stage process during which an initial gain fitting is applied according to a proprietary or device-independent method, followed by behavioral tests with the patient to fine-tune the fitting with the aim of maximizing speech intelligibility and listening comfort.Fitting methods usually prescribe frequency-dependent amplification on the basis of the patient’s audiometric thresholds
This study provides evidence that a reference-free automatic speech recognition (ASR) system can be used to improve individually prescribed HA gains
A possible concern was that any increase in the level at the output of the HA simulation would result in better ASR performance, by compensating for the “noise” induced by the age-related hearing loss (ARHL) simulation and improving the “signal-to-noise ratio.”
Summary
Fitting methods usually prescribe frequency-dependent amplification on the basis of the patient’s audiometric thresholds These target gains generally represent a compromise between providing enough amplification to restore audibility and taking into account the patient’s reduced dynamic range in order to avoid presenting sounds at uncomfortable levels. Some fitting methods, such as DSL V5 (Scollie et al, 2005), are based on a loudness-normalization (LN) rationale, aimed at restoring the loudness perception of the patient to that of a normal-hearing listener. The NAL-NL2 gain-prescription rule was defined by using an adaptive computer-controlled process to find optimal gains in terms of loudness and speech intelligibility, as measured by the Speech Intelligibility Index (ANSI, 1997)
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