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ID 56199
フルテキストURL
著者
Murakami, Hiroki
Hara, Sunao Graduate School of Natural Science and Technology, Okayama University
Abe, Masanobu Graduate School of Natural Science and Technology, Okayama University
Sato, Masaaki Graduate School of Medicine Dentistry and Pharmaceutical Sciences, Okayama University
Minagi, Shogo Graduate School of Medicine Dentistry and Pharmaceutical Sciences, Okayama University
抄録
In this paper, we propose an algorithm to improve the naturalness of the reconstructed glossectomy patient's speech that is generated by voice conversion to enhance the intelligibility of speech uttered by patients with a wide glossectomy. While existing VC algorithms make it possible to improve intelligibility and naturalness, the result is still not satisfying. To solve the continuing problems, we propose to directly modify the speech waveforms using a spectrum differential. The motivation is that glossectomy patients mainly have problems in their vocal tract, not in their vocal cords. The proposed algorithm requires no source parameter extractions for speech synthesis, so there are no errors in source parameter extractions and we are able to make the best use of the original source characteristics. In terms of spectrum conversion, we evaluate with both GMM and DNN. Subjective evaluations show that our algorithm can synthesize more natural speech than the vocoder-based method. Judging from observations of the spectrogram, power in high-frequency bands of fricatives and stops is reconstructed to be similar to that of natural speech.
キーワード
voice conversion
speech intelligibility
glossectomy
spectral differential
neural network
発行日
2018-09-02
出版物タイトル
Proceedings of Interspeech 2018
出版者
International Speech Communication Association
開始ページ
2464
終了ページ
2468
ISSN
1990-9772
資料タイプ
会議発表論文
言語
English
OAI-PMH Set
岡山大学
論文のバージョン
publisher
DOI
関連URL
isVersionOf https://doi.org/10.21437/Interspeech.2018-1239