Latent Correlation Analysis of HMM Parameters for Speech Recognition

Correlation between HMM parameters has been utilized for various rapid speaker adaptation, e.g. eigenvoice adaptation. The covariance matrix of the supervector which is a concatenation of all the Gaussian means in HMM, is clearly a good measure of such parameter correlation. In this paper, we propose to treat the supervector as a latent variable under HMM, and perform estimation of the hidden supervector´s covariance matrix directly from the acoustic frames using EM algorithm. In contrast to traditional methods which depend on using well-trained/adapted supervector samples, the proposed method is more theoretically sound and capable of dealing well with speaker-specific data sparseness. Moreover, the idea of conducting utterance-level correlation analysis, estimating utterance eigenvoices, and performing (unsupervised) utterance adaptation is explored. Experiments on the OGI Numbers database show that the proposed approach achieves better adaptation performance than the traditional methods, and the utterance-level correlation analysis is found to be useful.