EUROSPEECH 2003 - INTERSPEECH 2003
8th European Conference on Speech Communication and Technology

Geneva, Switzerland
September 1-4, 2003

        

Maximum A Posteriori Linear Regression (MAPLR) Variance Adaptation for Continuous Density HMMS

Wu Chou (1), Xiaodong He (2)

(1) Avaya Labs Research, USA
(2) University of Missouri, USA

In this paper, the theoretical framework of maximum a Posteriori linear regression (MAPLR) based variance adaptation for continuous density HMMs is described. In our approach, a class of informative prior distribution for MAPLR based variance adaptation is identified, from which the close form solution of MAPLR based variance adaptation is obtained under its EM formulation. Effects of the proposed prior distribution in MAPLR based variance adaptation are characterized and compared with conventional maximum likelihood linear regression (MLLR) based variance adaptation. These findings provide a consistent Bayesian theoretical framework to incorporate prior knowledge in linear regression based variance adaptation. Experiments on large vocabulary speech recognition tasks were performed. The experimental results indicate that significant performance gain over the MLLR based variance adaptation can be obtained based on the proposed approach.

Full Paper

Bibliographic reference.  Chou, Wu / He, Xiaodong (2003): "Maximum a posteriori linear regression (MAPLR) variance adaptation for continuous density HMMS", In EUROSPEECH-2003, 1513-1516.