A corpus-based speech synthesis system with emotion

Akemi Iida, Nick Campbell, Fumito Higuchi, Michiaki Yasumura

研究成果: Article

115 引用 (Scopus)

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We propose a new approach to synthesizing emotional speech by a corpus-based concatenative speech synthesis system (ATR CHATR) using speech corpora of emotional speech. In this study, neither emotional-dependent prosody prediction nor signal processing per se is performed for emotional speech. Instead, a large speech corpus is created per emotion to synthesize speech with the appropriate emotion by simple switching between the emotional corpora. This is made possible by the normalization procedure incorporated in CHATR that transforms its standard predicted prosody range according to the source database in use. We evaluate our approach by creating three kinds of emotional speech corpus (anger, joy, and sadness) from recordings of a male and a female speaker of Japanese. The acoustic characteristics of each corpus are different and the emotions identifiable. The acoustic characteristics of each emotional utterance synthesized by our method show clear correlations to those of each corpus. Perceptual experiments using synthesized speech confirmed that our method can synthesize recognizably emotional speech. We further evaluated the method's intelligibility and the overall impression it gives to the listeners. The results show that the proposed method can synthesize speech with a high intelligibility and gives a favorable impression. With these encouraging results, we have developed a workable text-to-speech system with emotion to support the immediate needs of nonspeaking individuals. This paper describes the proposed method, the design and acoustic characteristics of the corpora, and the results of the perceptual evaluations.

元の言語English
ページ(範囲)161-187
ページ数27
ジャーナルSpeech Communication
40
発行部数1-2
DOI
出版物ステータスPublished - 2003 4 1
外部発表Yes

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Communication
  • Language and Linguistics
  • Linguistics and Language
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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