A regularized discriminative training method of acoustic models derived by minimum relative entropy discrimination

Yotaro Kubo, Shinji Watanabe, Atsushi Nakamura, Tetsunori Kobayashi

Research output: Contribution to conferencePaper

5 Citations (Scopus)

Abstract

We present a realization method of the principle of minimum relative entropy discrimination (MRED) in order to derive a regularized discriminative training method. MRED is advantageous since it provides a Bayesian interpretations of the conventional discriminative training methods and regularization techniques. In order to realize MRED for speech recognition, we proposed an approximation method of MRED that strictly preserves the constraints used in MRED. Further, in order to practically perform MRED, an optimization method based on convex optimization and its solver based on the cutting plane algorithm are also proposed. The proposed methods were evaluated on continuous phoneme recognition tasks. We confirmed that the MRED-based training system outperformed conventional discriminative training methods in the experiments.

Original languageEnglish
Pages2954-2957
Number of pages4
Publication statusPublished - 2010 Dec 1
Event11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 - Makuhari, Chiba, Japan
Duration: 2010 Sep 262010 Sep 30

Conference

Conference11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010
CountryJapan
CityMakuhari, Chiba
Period10/9/2610/9/30

Keywords

  • Discriminative training
  • Optimization
  • Speech recognition

ASJC Scopus subject areas

  • Language and Linguistics
  • Speech and Hearing

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    Kubo, Y., Watanabe, S., Nakamura, A., & Kobayashi, T. (2010). A regularized discriminative training method of acoustic models derived by minimum relative entropy discrimination. 2954-2957. Paper presented at 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010, Makuhari, Chiba, Japan.