IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
... (for ESS/CS/ES/ISS)
Tech. Rep. Archives
... (for ES/CS)
 Go Top Page Go Previous   [Japanese] / [English] 

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.)
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

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  
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.)
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Date Time 2014-05-25 11:30:00 
Presentation Time 240 
Registration for SP 
Paper # IEICE-SP2014-34 
Volume (vol) IEICE-114 
Number (no) no.52 
Page pp.343-348 
#Pages IEICE-6 
Date of Issue IEICE-SP-2014-05-17 

[Return to Top Page]

[Return to IEICE Web Page]

The Institute of Electronics, Information and Communication Engineers (IEICE), Japan