A Discriminative Signal Subspace Speech Classifier

Abstract

A speech model inspired by the signal subspace methods was recently proposed as a speech classifier with modest results. Fashioned along a "best representation" approach, the absence of valuable interclass information in the speech model, however, impairs the ability of the classifier to distinguish between phonetically alike classes. This letter proposes an improved classifier that implements interclass information. Specifically, a measure of the discriminative quality of individual class elements is defined and determined for all class elements. The discrimination measures thus obtained are subsequently applied in the classification procedure. Simulation results of the proposed signal subspace classifier in an isolated digit speech recognition problem reveal an improved performance over its predecessor.