A composite signal subspace speech classifier

Abstract

Recently, a speech model inspired by signal subspace methods was proposed for a speech classifier. In using subspace information to characterize the speech signal, subspace trajectories in the form of the right singular vectors of the measurement matrices are obtained. Signal classification is thereafter accomplished by a minimum-distance rule with noteworthy results. This paper extends the foregoing approach by organizing the vector trajectories into matrices. The matrices so obtained are the reduced-rank approximation of the sample correlation matrices. A new dissimilarity measure in the Frobenius norm is correspondingly proposed for the matrix trajectories. Simulation results of the proposed composite signal subspace classifier in an isolated digit speech recognition problem reveal an improved performance over its predecessor. Additionally, the results also show the proposed classifier retaining the white noise robustness of the original design.