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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 researchmap
Abe, Masanobu Graduate school of Interdisciplinary Science and Engineering in Health Systems, Okayama University ORCID Kaken ID publons researchmap
Hojo, Nobukatsu NTT Corporation
Ijima, Yusuke NTT Corporation
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%.
キーワード
Emotional speech synthesis
Extrapolation
DNN-based TTS
Text-to-speech
Acoustic model
Phoneme duration model
発行日
2021-02
出版物タイトル
Speech Communication
126巻
出版者
Elsevier
開始ページ
35
終了ページ
43
ISSN
0167-6393
NCID
AA10630135
資料タイプ
学術雑誌論文
言語
English
OAI-PMH Set
岡山大学
論文のバージョン
author
DOI
Web of Science KeyUT
関連URL
isVersionOf https://doi.org/10.1016/j.specom.2020.11.004
ライセンス
https://creativecommons.org/licenses/by-nc-nd/4.0/