A non-linear efferent-inspired model of the auditory system; matching human confusions in stationary noise
Current predictors of speech intelligibility are inadequate for understanding and predicting speech confusions caused by acoustic interference. We develop a model of auditory speech processing that includes a phenomenological representation of the action of the Medial Olivocochlear efferent pathway and that is capable of predicting consonant confusions made by normal hearing listeners in speech-shaped Gaussian noise. We then use this model to predict human error patterns of initial consonants in consonantvowelconsonant words in the context of a Dynamic Rhyme Test. In the process we demonstrate its potential for speech discrimination in noise. Our results produced performance that was robust to varying levels of stationary additive speech-shaped noise and which mimicked human performance in discrimination of synthetic speech as measured by the Chi-squared test.
human confusions moc efferent speech recognition noise robustness mbpnl model