ID | 61445 |
フルテキストURL | |
著者 |
Inoue, Katsuki
Graduate school of Interdisciplinary Science and Engineering in Health Systems, Okayama University
Hara, Sunao
Graduate school of Interdisciplinary Science and Engineering in Health Systems, Okayama University
ORCID
Kaken ID
publons
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Abe, Masanobu
Graduate school of Interdisciplinary Science and Engineering in Health Systems, Okayama University
ORCID
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Hojo, Nobukatsu
NTT Corporation
Ijima, Yusuke
NTT Corporation
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抄録 | This paper proposes architectures that facilitate the extrapolation of emotional expressions in deep neural network (DNN)-based text-to-speech (TTS). In this study, the meaning of “extrapolate emotional expressions” is to borrow emotional expressions from others, and the collection of emotional speech uttered by target speakers is unnecessary. Although a DNN has potential power to construct DNN-based TTS with emotional expressions and some DNN-based TTS systems have demonstrated satisfactory performances in the expression of the diversity of human speech, it is necessary and troublesome to collect emotional speech uttered by target speakers. To solve this issue, we propose architectures to separately train the speaker feature and the emotional feature and to synthesize speech with any combined quality of speakers and emotions. The architectures are parallel model (PM), serial model (SM), auxiliary input model (AIM), and hybrid models (PM&AIM and SM&AIM). These models are trained through emotional speech uttered by few speakers and neutral speech uttered by many speakers. Objective evaluations demonstrate that the performances in the open-emotion test provide insufficient information. They make a comparison with those in the closed-emotion test, but each speaker has their own manner of expressing emotion. However, subjective evaluation results indicate that the proposed models could convey emotional information to some extent. Notably, the PM can correctly convey sad and joyful emotions at a rate of >60%.
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キーワード | Emotional speech synthesis
Extrapolation
DNN-based TTS
Text-to-speech
Acoustic model
Phoneme duration model
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発行日 | 2021-02
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出版物タイトル |
Speech Communication
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巻 | 126巻
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出版者 | Elsevier
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開始ページ | 35
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終了ページ | 43
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ISSN | 0167-6393
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NCID | AA10630135
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資料タイプ |
学術雑誌論文
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言語 |
英語
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OAI-PMH Set |
岡山大学
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論文のバージョン | author
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DOI | |
Web of Science KeyUT | |
関連URL | isVersionOf https://doi.org/10.1016/j.specom.2020.11.004
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ライセンス | https://creativecommons.org/licenses/by-nc-nd/4.0/
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