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.
[1]
Mark J. F. Gales,et al.
Mean and variance adaptation within the MLLR framework
,
1996,
Comput. Speech Lang..
[2]
Chin-Hui Lee,et al.
Extended maximum a posterior linear regression (EMAPLR) model adaptation for speech recognition
,
2000,
INTERSPEECH.
[3]
Wu Chou,et al.
Maximum a posterior linear regression with elliptically symmetric matrix variate priors
,
1999,
EUROSPEECH.
[4]
Philip C. Woodland,et al.
Speaker adaptation of HMMs using linear regression
,
1994
.
[5]
Philip C. Woodland,et al.
Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models
,
1995,
Comput. Speech Lang..