Speech recognition based on student's t-distribution derived from total Bayesian framework

Shinji Watanabe*, Atsushi Nakamura

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)970-980
Number of pages11
JournalIEICE Transactions on Information and Systems
Issue number3
Publication statusPublished - 2006
Externally publishedYes


  • Bayesian prediction
  • Speech recognition
  • Student's t-distribution
  • Total Bayesian framework VBEC

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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