Rapid speaker adaptation using a priori knowledge by eigenspace analysis of MLLR parameters

This paper considers the problem of rapid speaker adaptation in speech recognition. In particular, we exploit an approach based on combination of transformations, which utilizes the concepts of both maximum likelihood linear regression (MLLR) and eigenvoice adaptation. We analyze three different possible methods to realize the concept, and formulate a fast algorithm of maximum likelihood coefficient estimation for test speakers. It is found that the best approach can properly utilize the a priori knowledge of speaker-independent models in constructing the eigenspace for speaker characteristics, while using MLLR matrices in representing the specific speakers so as to reduce the on-line memory and computation requirement of the adaptation phase. This best approach leads to identical models relative to eigenvoice adaptation that is based on MLLR-adapted speaker models. The experimental results and discussions also provide a good analysis towards integration of the MLLR and eigenvoice approaches