Structuring linear transforms for adaptation using training time information

Abstract

Linear transforms are often used for adaptation to test data in speech recognition systems. However, when used with small amounts of test data, these techniques provide limited improvements if any. This paper proposes a two-step Bayesian approach where a) the transforms lie in a subspace obtained at training time and b) the expansion coefficients of the transform are obtained using MAP. Estimation algorithms are given for adaptation transforms for means, covariances, and feature spaces. Experimental results indicate that our method gives a significant improvement in performance over other methods.