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Speech Recognition Based on Student's t-Distribution Derived from Total Bayesian Framework Shinji WATANABE Atsushi NAKAMURA Publication IEICE TRANSACTIONS on Information and Systems Vol.E89-D No.3 pp.970-980 Publication Date: 2006/03/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e89-d.3.970 Print ISSN: 0916-8532 Type of Manuscript: Special Section PAPER (Special Section on Statistical Modeling for Speech Processing) Category: Speech Recognition Keyword: speech recognition, total Bayesian framework VBEC, Bayesian prediction, student's t-distribution, Full Text: PDF(574.8KB)>>
Summary: We introduce a robust classification method based on the Bayesian predictive distribution (Bayesian Predictive Classification, referred to as BPC) for speech recognition. We and others have recently proposed a total Bayesian framework named Variational Bayesian Estimation and Clustering for speech recognition (VBEC). VBEC includes the practical computation of approximate posterior distributions that are essential for BPC, based on variational Bayes (VB). BPC using VB posterior distributions (VB-BPC) provides an analytical solution for the predictive distribution as the Student's t-distribution, which can mitigate the over-training effects by marginalizing the model parameters of an output distribution. We address the sparse data problem in speech recognition, and show experimentally that VB-BPC is robust against data sparseness. | ||||||||||
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