Bayesian approaches to acoustic modeling: A review

Shinji Watanabe*, Atsushi Nakamura


研究成果: Review article査読

3 被引用数 (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.

ジャーナルAPSIPA Transactions on Signal and Information Processing
出版ステータスPublished - 2012 12月

ASJC Scopus subject areas

  • 信号処理
  • 情報システム


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