Compact acoustic modeling based on acoustic manifold using a mixture of factor analyzers

Abstract

A compact acoustic model for speech recognition is proposed based on nonlinear manifold modeling of the acoustic feature space. Acoustic features of the speech signal is assumed to form a low-dimensional manifold, which is modeled by a mixture of factor analyzers. Each factor analyzer describes a local area of the manifold using a low-dimensional linear model. For an HMM-based speech recognition system, observations of a particular state are constrained to be located on part of the manifold, which may cover several factor analyzers. For each tied-state, a sparse weight vector is obtained through an iteration shrinkage algorithm, in which the sparseness is determined automatically by the training data. For each nonzero component of the weight vector, a low-dimensional factor is estimated for the corresponding factor model according to the maximum a posteriori (MAP) criterion, resulting in a compact state model. Experimental results show that compared with the conventional HMM-GMM system and the SGMM system, the new method not only contains fewer parameters, but also yields better recognition results.