Speech enhancement based on a voiced-unvoiced speech model

Abstract

In this work, we attempt to refine the methods based on autoregressive (AR) modeling for speech enhancement (Paliwal and Basu, 1987); Gibson et al., 1991). As a matter of fact, AR modelling, which is a key strategy of the two above methods, is known to be good for representing unvoiced speech but not quite appropriate for voiced speech which is quite periodic in nature. Here, we incorporate a speech model which satisfactorily describes voiced and unvoiced speeches and silence (i.e., pauses between speech utterances) into the enhancement framework developed in the two above methods, and specifically devise an algorithm for computing the optimal estimate of the clean speech in the minimum-mean-square-error sense. We also present the methods we use for estimating the model parameters and give a description of the complete enhancement procedure. Performance assessment based on spectrogram plots, objective measures and informal subjective listening tests all indicate that our method gives consistently good results.