Speech Recognition Based on Student's t-Distribution Derived from Total Bayesian Framework(Speech Recognition, <Special Section> Statistical Modeling for Speech Processing)
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概要
- 論文の詳細を見る
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 overtraining 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.
- 社団法人電子情報通信学会の論文
- 2006-03-01
著者
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Nakamura Atsushi
Ntt Communication Science Laboratories Ntt Corporation
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WATANABE Shinji
NTT Communication Science Laboratories, NTT Corporation
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Watanabe Shinji
Ntt Communication Science Laboratories Ntt Corporation
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- Speech Recognition Based on Student's t-Distribution Derived from Total Bayesian Framework(Speech Recognition, Statistical Modeling for Speech Processing)
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