Comparison of the predictive accuracy of different computational models of auditory perception

Abstract

Computational models of auditory perception offer a time-efficient method of assessing the effects of distortion on speech perception. Several objective metrics have been proposed to predict speech intelligibility, especially when speech is obscured by the presence of background noise. Novel approaches to full-reference and reference-free speech intelligibility metrics have emerged in recent years, but deciphering the best metric for predicting speech intelligibility still requires investigation. This study assessed the predictive accuracy of several reliable, full, and reference-free speech intelligibility metrics. Speech perception scores were measured on listeners with normal hearing and hearing loss in quiet and noise. Acoustic recordings were made of the presented speech stimuli and combined with a computational model of the auditory nerve to simulate behavioral scores using several established metrics such as the STOI, NSIM, SRMR, SII, SNRloss, and BSIM. The estimated speech scores were correlated with behavioral speech recognition scores to assess predictive accuracy of the model simulations. Several of the predicted scores correlated well with behavioral scores. Evaluation of individual phonemes revealed differential sensitivity of the metrics across different phonemic classifications.