Recent studies have shown that automatic speech recognition (ASR) can be used, in combination with signal-processing algorithms mimicking the effects of hearing loss (HL), to predict the speech-identification performances of older hearing-impaired (OHI) listeners. Repeating the random search yielded similar ASR scores, but different time-constant configurations. Optimizing the time constants significantly improved the ASR scores when CAM2 insertion gains were used, but not when using ASR-based gains. To assess the reproducibility of the results, the random search algorithm was run twice. For each audiogram, 1,000 iterations of the random-search algorithm were used to find the time-constant configuration yielding the highest ASR score. The resulting speech signals were fed to an ASR system for recognition.
Speech timer 1500 simulator#
A HA simulator and a HL simulator simulator were used, respectively, to amplify and to degrade speech stimuli according to the input audiogram. For each audiometric profile, the random-search algorithm was used to vary time constants with the aim to maximize ASR performance. 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. 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. 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. 4School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, United KingdomĪutomatic 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.3Manchester Centre for Audiology and Deafness, School of Health Sciences, University of Manchester, Manchester, United Kingdom.2IRIT, CNRS, Université Paul Sabatier, Toulouse, France.Lionel Fontan 1*†, Libio Gonçalves Braz 2*†, Julien Pinquier 2, Michael A.
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