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Paper Abstract and Keywords
Presentation 2014-05-25 11:30
A joint restricted Boltzmann machine for dictionary learning in sparse-representation-based voice conversion
Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) SP2014-34
Abstract (in Japanese) (See Japanese page) 
(in English) In voice conversion, sparse-representation-based methods have recently been garnering attention because they are, relatively speaking, not affected by over-fitting or over-smoothing problems. In these approaches, voice conversion is achieved by estimating a sparse vector that determines which dictionaries of the target speaker should be used, calculated from the matching of the input vector and dictionaries of the source speaker. The sparse-representation-based voice conversion methods can be broadly divided into two approaches: 1) an approach that uses raw acoustic features in the training data as parallel dictionaries, and 2) an approach that trains parallel dictionaries from the training data. Our approach belongs to the latter; we systematically estimate the parallel dictionaries using a restricted Boltzmann machine, a fundamental technology commonly used in deep learning. Through voice-conversion experiments, we confirmed the high-performance of our method, comparing it with the conventional Gaussian mixture model (GMM)-based approach, and a non-negative matrix factorization (NMF)-based approach, which is based on sparse-representation.
Keyword (in Japanese) (See Japanese page) 
(in English) Voice conversion / restricted Boltzmann machine / sparse representation / parallel dictionary learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 52, SP2014-34, pp. 343-348, May 2014.
Paper # SP2014-34 
Date of Issue 2014-05-17 (SP) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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 SP IPSJ-MUS  
Conference Date 2014-05-24 - 2014-05-25 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2014-05-SP-MUS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A joint restricted Boltzmann machine for dictionary learning in sparse-representation-based voice conversion 
Sub Title (in English)  
Keyword(1) Voice conversion  
Keyword(2) restricted Boltzmann machine  
Keyword(3) sparse representation  
Keyword(4) parallel dictionary learning  
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1st Author's Name Toru Nakashika  
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 2014-05-25 11:30:00 
Presentation Time 240 minutes 
Registration for SP 
Paper # SP2014-34 
Volume (vol) vol.114 
Number (no) no.52 
Page pp.343-348 
#Pages 6 
Date of Issue 2014-05-17 (SP) 

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