Variance compensation within the MLLR framework for robust speech recognition and speaker adaptation

This paper investigates the use of maximum likelihood linear regression (MLLR) for both speaker and environment adaptation. MLLR transforms the mean and variance parameters of a set of HMMs. In this paper a number of different types of linear transformations of the variances are examined including full, block diagonal, and diagonal transformation matrices. Experiments on large vocabulary speaker independent data sets are described. On all the data sets examined the use of MLLR mean and variance compensation reduced the error rate compared to mean-only compensation. Furthermore, the use of a block diagonal or full transformation of the variances on the clean data task showed slight improvements over the diagonal case. However, when some environmental mismatch was present then was no difference in performance between using multiple diagonal variance transformations and a more complex single variance transform.