GLS-based TV-CAR speech analysis using forward and backward linear prediction

Abstract

We have already proposed novel robust parameter estimation algorithms of a time-varying complex AR (TV-CAR) model for analytic speech signals, which are based on GLS (generalized least squares) and ELS (extended least squares) and have shown that the methods can achieve robust speech spectrum estimation against additive white Gaussian. In these methods, forward prediction error is only used to calculate the MSE criterion. This paper proposes the improved TV-CAR speech analysis methods based on forward and backward linear prediction in which backward prediction error is also adopted to calculate the MSE criterion, viz., the MMSE and GLS-based algorithms using the forward and backward prediction. The experiments with natural speech and natural speech corrupted by white Gaussian demonstrate that the improved methods can achieve more accurate and more stable spectral estimation.