Conversational spontaneous speech synthesis using average voice model

Tomoki Koriyama, Takashi Nose, Takao Kobayashi

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

This paper describes conversational spontaneous speech synthesis based on hidden Markov model (HMM). To reduce the amount of data required for model training, we utilize an average-voice-based speech synthesis framework, which has been shown to be effective for synthesizing speech with arbitrary speaker's voice using a small amount of training data. We examine several kinds of average voice model using reading-style speech and/or conversation-style speech. We also examine an appropriate utterance unit for conversational speech synthesis. Experimental results show that the proposed two-stage model adaptation method improves the quality of synthetic conversational speech.

本文言語English
ホスト出版物のタイトルProceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010
出版社International Speech Communication Association
ページ853-856
ページ数4
出版ステータスPublished - 2010
外部発表はい

出版物シリーズ

名前Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010

ASJC Scopus subject areas

  • 言語および言語学
  • 言語聴覚療法

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