Online speaker adaptation based on quasi-Bayes linear regression

This paper presents an online/sequential linear regression adaptation framework for hidden Markov model (HMM) based speech recognition. Our attempt is to sequentially improve the speaker-independent (SI) speech recognizer to meet nonstationary environments via linear regression adaptation of SI HMMs. A quasi-Bayes linear regression (QBLR) algorithm is developed to execute online adaptation where the regression matrix is estimated using QB theory. In the estimation, we moderately specify the prior density of the regression matrix as a matrix variate normal distribution and exactly derive the pooled posterior density belonging to the same distribution family. Accordingly, the optimal regression matrix can be easily calculated. Also, the reproducible prior/posterior density pair provides a meaningful mechanism for sequential learning of prior statistics. At each sequential epoch, only the updated prior statistics and the current observed data are required for adaptation. In general, the proposed QBLR is universal and can be reduced to the well-known maximum likelihood linear regression (MLLR) and maximum a posteriori linear regression (MAPLR). Experiments show that the QBLR is effective for speaker adaptation in car environments.