Bayesian approaches to acoustic modeling: A review

Shinji Watanabe*, Atsushi Nakamura

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)


This paper focuses on applications of Bayesian approaches to acoustic modeling for speech recognition and related speechprocessing applications. Bayesian approaches have been widely studied in the fields of statistics and machine learning, and one of their advantages is that their generalization capability is better than that of conventional approaches (e.g., maximum likelihood). On the other hand, since inference in Bayesian approaches involves integrals and expectations that are mathematically intractable in most cases and require heavy numerical computations, it is generally difficult to apply them to practical speech recognition problems.However, there have beenmany such attempts, and this paper aims to summarize these attempts to encourage further progress on Bayesian approaches in the speech-processing field. This paper describes various applications of Bayesian approaches to speech processing in terms of the four typical ways of approximating Bayesian inferences, i.e., maximum a posteriori approximation, model complexity control using a Bayesian information criterion based on asymptotic approximation, variational approximation, and Markov chain Monte Carlo-based sampling techniques.

Original languageEnglish
Article numbere5
JournalAPSIPA Transactions on Signal and Information Processing
Publication statusPublished - 2012 Dec
Externally publishedYes


  • Approximate bayesian inference
  • Bayesian approach
  • Machine learning
  • Speech processing

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems


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