Single acoustic-channel speech enhancement based on glottal correlation using non-acoustic sensor

Abstract

This paper describes a single acoustic–channel speech enhancement, utilizing an auxiliary non-acoustic sensor. Unlike classical algorithms, which make use of the knowledge from acoustic signal alone, the glottal correlation (GCORR) algorithm takes advantage of non-acoustic throat sensors such as the general electromagnetic motion sensor (GEMS). The non–acoustic sensor provides a measure of the glottal excitation function that is relatively immune to background acoustic noise. Thus, inspired by human speech production mechanisms, the GCORR algorithm extracts the desired speech signal from noisy acoustic mixture using statistical correlation between the speech and its excitation. The algorithm leads to a significant reduction of wide–band noise, even when the SNR is very low. The improvement in the quality of the speech is demonstrated in terms of an objective evaluation.