Using class weighting in inter-class MLLR

A new adaptation method called inter-class MLLR has recently been introduced. Inter-class MLLR utilizes relationships among different transformation functions to achieve more reliable estimates of MLLR parameters across multiple classes, and it produces lower word error rates (WER) than conventional MLLR in circumstances where very little speaker-specific adaptation data are available. This paper describes the application of weights to the neighboring classes to improve the effectiveness with which they are combined with the target class in inter-class MLLR. These weights are obtained from the variance of the estimation error considering the weighted least squares estimation in classical linear regression. In our experiments, the weights provided small improvements in WER for supervised adaptation but almost no improvement in unsupervised adaptation using only a small amount of adaptation data. We also discuss the effect of decreasing the number of neighboring classes as more adaptation data become available, the development of inter-class transformations from the test speaker, and the combination of inter-class MLLR with principal-component MLLR. None of the feasible variations of weighted inter-class MLLR provided significant improvements to recognition accuracy.