Robust Model Adaptation Using Mean and Variance Transformations in Linear Spectral Domain

Abstract

In this paper, we propose robust speech adaptation technique using continuous density hidden Markov models (HMMs) in unknown environments. This adaptation technique is an improved maximum likelihood linear spectral transformation (ML-LST) method, which aims to find appropriate noise parameters in the linear spectral domain. Previously, ML-LST and many transform-based adaptation algorithms have been applied to the Gaussian mean vectors of HMM systems. In the improved ML-LST for the rapid adaptation, the mean vectors and covariance matrices of an HMM based speech recognizer are transformed simultaneously using a small number of transformation parameters. It is shown that the variance transformation provides important information which can be used to handle environmental noise, in the similar manner that the mean transformation does.