A signal subspace approach for speech modelling and classification

Abstract

In this paper, a speech classifier inspired by the signal subspace approach is developed. A novel signal subspace speech model is initially obtained via a rank reducing subspace decomposition algorithm that is based on the SVD. Motivated by the assumption that the speech signal comprises of short term dynamics that are slowly changing, it follows that the signal subspace of the speech signal is likewise slowly changing. The proposed signal subspace model aims to characterize the subspace dynamics using a family of subspace trajectories. In particular, each subspace trajectory is a sequence of vectors that traces the dynamics of a rank-one subspace in time. An assembly of these trajectories, henceforth, specifies the progression of the embedded signal subspace. To construct the signal subspace classifier, prototype elements in the form of the signal subspace models are determined for every signal class. A minimum-distance rule with a distance measure that resembles an energy difference function is subsequently applied in the actual classification task. Simulation of the proposed signal subspace classifier in an isolated digit speech recognition problem reveals promising results.