Two-stage speaker adaptation in subspace Gaussian mixture models

A two-stage speaker adaptation approach is proposed for the subspace Gaussian mixture model (SGMM) [1] in large vocabulary automatic speech recognition (ASR). The SGMM differs from the more well known continuous density hidden Markov model (CDHMM) in that a large portion of the SGMM parameters are dedicated to shared full covariance Gaussian subspace parameters and a relatively small number of parameters are used for state dependent projection vectors. Both model space and feature space adaptation are investigated. First, an efficient regression based approach for subspace vector adaptation (SVA) is presented. Second, an efficient approach is presented for feature space adaptation using constrained maximum likelihood linear regression (CMLLR) in the SGMM. While both of these adaptation scenarios have previously been investigated in the context of the SGMM [2, 3], a more efficient and numerically stable procedure is presented here for estimating the parameters of the regression based transformations. Both transformation matrices are obtained using an optimization technique that iteratively updates the rows of the regression matrices. It is shown that using these feature space and model space approaches for unsupervised speaker adaptation provides complementary improvements in SGMM based ASR word accuracy.