An on-line adaptation technique for emotional speech recognition using style estimation with multiple-regression HMM

Yusuke Ijima, Makoto Tachibana, Takashi Nose, Takao Kobayashi

研究成果: Conference article査読

3 被引用数 (Scopus)

抄録

This paper describes a model adaptation technique for emotional speech recognition based on multiple-regression HMM (MR-HMM).We use a low-dimensional vector called style vector which corresponds the degree of expressivity of emotional speech as the explanatory variable of the regression. In the proposed technique, first, the value of the style vector for input speech is estimated. Then, using the estimated style vector, new mean vectors of the output distributions of HMM are adapted to the input style. The style vector is estimated every input utterance, and an on-line adaptation can be done in each utterance. We perform phoneme recognition experiments for professional narrators' acted speech and evaluate the performance by comparing with style-dependent and style-independent HMMs. Experimental results show the proposed technique reduced the error rates by 11% of the style-independent model.

本文言語English
ページ(範囲)1297-1300
ページ数4
ジャーナルProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
出版ステータスPublished - 2008 12 1
外部発表はい
イベントINTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association - Brisbane, QLD, Australia
継続期間: 2008 9 222008 9 26

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Sensory Systems

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