Specific distance for feature selection in speech recognition

Abstract

In this paper, the use of a specific metric as a feature selection step is investigated. The feature selection step tries to model the correlation among adjacent feature vectors and the variability of the speech. We propose a new procedure which performs the feature selection in two steps. The first step takes into account the temporal correlation among the N feature vectors of a template in order to obtain a new set of feature vectors which are uncorrelated. This step gives a new template of M feature vectors, with M « N. The second step defines a specific distance among feature vectors to take into account the frequency discrimination features which discriminate each word of the vocabulary from the others or a set of them. Thus, the new feature vectors are uncorrelated in time and discriminant in frequency.