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Paper Abstract and Keywords
Presentation 2014-12-16 11:00
Many-to-one Voice Conversion using Multiple Non-negative Matrix Factorization
Ryo Aihara, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) SP2014-114
Abstract (in Japanese) (See Japanese page) 
(in English) Voice conversion (VC) is being widely researched in the field of speech processing because of increased interest in using such processing in applications such as personalized Text-To-Speech systems. Statistical approach using Gaussian Mixture Model (GMM) is widely researched in VC and eigen-voice GMM enables one-to-many and many-to-one VC from multiple training data sets. We present in this paper an exemplar-based VC method using Non-negative Matrix Factorization (NMF), which is different from conventional statistical VC. NMF-based VC has advantages of noise robustness and naturalness of converted voice compared to GMM-based VC. However, because NMF-based VC is based on parallel training data of source and target speaker, we cannot covert voice of arbitrary speakers in this framework. In this paper, we propose a many-to-one VC using Multiple Non-negative Matrix Factorization (Multi-NMF). By using Multi-NMF, arbitrary speaker's voice is converted to target speaker's voice without any training data of input speaker's. We assume that this method is flexible because we can adopt it to many-to-many VC or voice quality control.
Keyword (in Japanese) (See Japanese page) 
(in English) Voice Conversion / Speech synthesis / Non-negative Matrix Factorization / Exemplar-based / Many-to-one / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 365, SP2014-114, pp. 75-80, Dec. 2014.
Paper # SP2014-114 
Date of Issue 2014-12-08 (SP) 
ISSN Print edition: ISSN 0913-5685    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)
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Conference Information
Committee NLC IPSJ-NL SP IPSJ-SLP JSAI-SLUD  
Conference Date 2014-12-15 - 2014-12-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Tokyo Institute of Technology (Suzukakedai Campus) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The 6th Symposium on Collective Knowlege 
Paper Information
Registration To SP 
Conference Code 2014-12-NLC-NL-SP-SLP-SLUD 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Many-to-one Voice Conversion using Multiple Non-negative Matrix Factorization 
Sub Title (in English)  
Keyword(1) Voice Conversion  
Keyword(2) Speech synthesis  
Keyword(3) Non-negative Matrix Factorization  
Keyword(4) Exemplar-based  
Keyword(5) Many-to-one  
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1st Author's Name Ryo Aihara  
1st Author's Affiliation Kobe University (Kobe Univ.)
2nd Author's Name Tetsuya Takiguchi  
2nd Author's Affiliation Kobe University (Kobe Univ.)
3rd Author's Name Yasuo Ariki  
3rd Author's Affiliation Kobe University (Kobe Univ.)
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Speaker Author-1 
Date Time 2014-12-16 11:00:00 
Presentation Time 90 minutes 
Registration for SP 
Paper # SP2014-114 
Volume (vol) vol.114 
Number (no) no.365 
Page pp.75-80 
#Pages 6 
Date of Issue 2014-12-08 (SP) 

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