Inter-class MLLR for speaker adaptation

This paper examines the use of interdependencies of parameter classes in transformation-based speaker adaptation algorithms such as maximum likelihood linear regression (MLLR). In transformation-based adaptation, increasing the number of transformation classes can provide more detailed information for adaptation, but at the expense of greater estimation error with small amounts of data. In this paper we introduce a new procedure, inter-class MLLR, which utilizes the relationship between different classes to achieve both detailed and reliable transformation-based adaptation using limited data, In this method, the inter-class relation is given by a linear regression which is estimated from training data. In experiments using non-native English speakers from the Spoke 3 data in the 1994 DARPA Wall Street Journal evaluation, inter-class MLLR provided a relative reduction in word error rates of 11.3% compared to conventional MLLR.