A correlational discriminant approach to feature extraction for robust speech recognition

Abstract

A nonlinear discriminant analysis based approach to feature space dimensionality reduction in noise robust automatic speech recognition (ASR) is proposed. It utilizes a correlation based distance measure instead of the conventional Euclidean distance. The use of this 'correlation preserving discriminant analysis' (CPDA) procedure is motivated by evidence suggesting that correlation based cepstrum distance measures can be more robust than Euclidean based distances when speech is corrupted by noise. The performance of CPDA is evaluated in terms of the word error rate obtained by using CPDA derived features on a speech in noise task, and is compared to a number of Euclidean distance based approaches to feature space transformations, namely linear discriminant analysis (LDA), locality preserving projections (LPP), and locality preserving discriminant analysis (LPDA).