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Paper Abstract and Keywords
Presentation 2018-07-02 15:50
Transforming the Emotion in Speech using CycleGAN
Kenji Yasuda, Ryohei Orihara, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga (UEC) AI2018-11
Abstract (in Japanese) (See Japanese page) 
(in English) In domain transfer task deep learning makes it possible to generate more natural and highly accurate output. Especially with the advent of GAN(Generative Adversarial Network), Learning of transfers between unspecified domains has become possible. Voice conversion is an example of domain transformation for speech. Voice conversion can be paraphrased as speaker domain transformation, which many studies has been done. However, few studies have focused on transformations other than speakers. When aiming at more natural speech synthesis, it is necessary to study transformations other than speaker. Therefore, In this research, we use a model called CycleGAN to perform voice conversion on emotions. We selected "ANG(anger)", "JOY(joy)", "SAD(sadness)" as a conversion target. As a result of evaluation experiments, the model performs well on conversion to "ANG(anger)". In addition, the model performs well on conversion from "JOY(joy)".
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Learning / Domain Transfer / Generative Adversarial Network / Voice Conversion / Speech Processing / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 116, AI2018-11, pp. 61-66, July 2018.
Paper # AI2018-11 
Date of Issue 2018-06-25 (AI) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF AI2018-11

Conference Information
Committee AI  
Conference Date 2018-07-02 - 2018-07-02 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To AI 
Conference Code 2018-07-AI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Transforming the Emotion in Speech using CycleGAN 
Sub Title (in English)  
Keyword(1) Deep Learning  
Keyword(2) Domain Transfer  
Keyword(3) Generative Adversarial Network  
Keyword(4) Voice Conversion  
Keyword(5) Speech Processing  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Kenji Yasuda  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Ryohei Orihara  
2nd Author's Affiliation The University of Electro-Communications (UEC)
3rd Author's Name Yuichi Sei  
3rd Author's Affiliation The University of Electro-Communications (UEC)
4th Author's Name Yasuyuki Tahara  
4th Author's Affiliation The University of Electro-Communications (UEC)
5th Author's Name Akihiko Ohsuga  
5th Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2018-07-02 15:50:00 
Presentation Time 25 minutes 
Registration for AI 
Paper # AI2018-11 
Volume (vol) vol.118 
Number (no) no.116 
Page pp.61-66 
#Pages 6 
Date of Issue 2018-06-25 (AI) 

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