Conversational spontaneous speech synthesis using average voice model

Tomoki Koriyama, Takashi Nose, Takao Kobayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010
PublisherInternational Speech Communication Association
Pages853-856
Number of pages4
Publication statusPublished - 2010

Publication series

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

Keywords

  • Average voice model
  • Conversational speech
  • HMM-based speech synthesis
  • Speaker adaptation
  • Spontaneous speech
  • Style adaptation

ASJC Scopus subject areas

  • Language and Linguistics
  • Speech and Hearing

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