NDLI: Latent Correlation Analysis of HMM Parameters for Speech Recognition

Content Provider	IEEE Xplore Digital Library
Author	Zhijian Ou Jun Luo
Copyright Year	2007
Description	Author affiliation: Dept. of Electron. Eng., Tsinghua Univ., Beijing, China (Zhijian Ou; Jun Luo)
Abstract	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.
File Size	4927782
File Format	PDF
ISBN	1424407273
ISSN	15206149
DOI	10.1109/ICASSP.2007.367022
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech analysis Hidden Markov models Speech recognition Covariance matrix Loudspeakers Performance analysis Databases Decoding Principal component analysis Acoustic measurements eigenvoice correlation analysis HMM
Content Type	Text
Resource Type	Article

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