Quadratic classifier with sliding training data set in robust recursive AR speech analysis

Abstract

We propose a robust recursive procedure, based on a weighted recursive least squares (WRLS) algorithm with variable forgetting factor (VFF) and a quadratic classifier with sliding training data set, for identification of non-stationary autoregressive (AR) model of speech production system. Experimental evaluation is done using the results obtained by analyzing speech signal with voiced and mixed excitation frames. Experimental results have shown that the proposed robust recursive procedure achieves more accurate AR speech parameter estimates and provides improved tracking performance.