Sparse Bayesian factor analysis for stereo-based stochastic mapping

Abstract

This paper investigates a factor analysis scheme in the joint channel space of stereo-based stochastic mapping (SSM) for noise robust automatic speech recognition. A mixture of Bayesian factor analyzers is used to describe the generative factors in the multi-conditional training scenario in terms of noise type and signal-to-noise ratio. Sparsity-promoting prior is applied on the matrix of factor loadings to automatically learn the effective factors from a redundant dictionary in a particular soft cluster. Experiments carried out on large vocabulary continuous speech recognition tasks show that this sparse Bayesian factor analysis scheme leads to superior SSM performance for noise robustness.