An estimation technique of style expressiveness for emotional speech using model adaptation based on multiple-regression HSMM

Takashi Nose, Yoichi Kato, Makoto Tachibana, Takao Kobayashi

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

This paper describes a technique of estimating style expressiveness for an arbitrary speaker's emotional speech. In the proposed technique, the style expressiveness, representing how much the emotions and/or speaking styles affect the acoustic features, is estimated based on multiple-regression hidden semi-Markov model (MRHSMM). In the model training, we first train average voice model using multiple speakers' neutral style speech. Then, the speaker- and style-adapted HSMMs are obtained based on linear transformation from the average voice model with a small amount of the target speaker's data. Finally, MRHSMM of the target speaker is obtained using the adapted models. For given input emotional speech, the style expressiveness is estimated based on maximum likelihood criterion. From the experimental results, we show that the estimated value gives good correspondence to the perceptual rating.

Original languageEnglish
Pages (from-to)2759-2762
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 2008 Dec 1
Externally publishedYes
EventINTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association - Brisbane, QLD, Australia
Duration: 2008 Sep 222008 Sep 26

Keywords

  • Emotional expression
  • Estimation of expressiveness
  • Model adaptation
  • Multiple-regression HSMM

ASJC Scopus subject areas

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

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