Variational Bayesian estimation and clustering for speech recognition

Shinji Watanabe*, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

82 Citations (Scopus)


In this paper, we propose variational Bayesian estimation and clustering for speech recognition (VBEC), which is based on the variational Bayesian (VB) approach. VBEC is a total Bayesian framework: all speech recognition procedures (acoustic modeling and speech classification) are based on VB posterior distribution, unlike the maximum likelihood (ML) approach based on ML parameters. The total Bayesian framework generates two major Bayesian advantages over the ML approach for the mitigation of over-training effects, as it can select an appropriate model structure without any data set size condition, and can classify categories robustly using a predictive posterior distribution. By using these advantages, VBEC: 1) allows the automatic construction of acoustic models along two separate dimensions, namely, clustering triphone hidden Markov model states and determining the number of Gaussians and 2) enables robust speech classification, based on Bayesian predictive classification using VB posterior distributions. The capabilities of the VBEC functions were confirmed in large vocabulary continuous speech recognition experiments for read and spontaneous speech tasks. The experiments confirmed that VBEC automatically constructed accurate acoustic models and robustly classified speech, i.e., totally mitigated the over-training effects with high word accuracies due to the VBEC functions.

Original languageEnglish
Pages (from-to)365-381
Number of pages17
JournalIEEE Transactions on Speech and Audio Processing
Issue number4
Publication statusPublished - 2004 Jul
Externally publishedYes


  • Acoustic model selection
  • Bayesian predictive classification
  • Speech recognition
  • Total Bayesian framework
  • Variational Bayes

ASJC Scopus subject areas

  • Software
  • Acoustics and Ultrasonics
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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