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Paper Abstract and Keywords
Presentation 2018-06-28 15:10
Multimodal voice conversion using deep bottleneck features and deep canonical correlation analysis
Satoshi Tamura, Kento Horio, Hajime Endo, Satoru Hayamizu (Gifu Univ.), Tomoki Toda (Nagoya Univ.) PRMU2018-24 SP2018-4
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we aim at improving the speech quality in voice conversion and propose a novel multi-modal voice conversion approach using speech waveforms and lip images.
We employ deep bottleneck features to improve visual features in audio-visual voice conversion.
In addition, we also apply deep canonical correlation analysis to obtain much better audio and visual representations, as well as to build a new cross-modal framework.
We conducted subjective and objective evaluations in noisy environments to clarify usefulness of our proposed method, comparing to audio-only, visual-only and conventional audio-visual voice conversion schemes.
We then found our method can significantly improve the quality even in heavily noisy conditions.
Keyword (in Japanese) (See Japanese page) 
(in English) Voice conversion / multi-modal / audio-visual / cross-modal / deep learning / bottleneck feature / canonical component analysis /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 112, SP2018-4, pp. 13-18, June 2018.
Paper # SP2018-4 
Date of Issue 2018-06-21 (PRMU, SP) 
ISSN 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)
Download PDF PRMU2018-24 SP2018-4

Conference Information
Committee PRMU SP  
Conference Date 2018-06-28 - 2018-06-29 
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 2018-06-PRMU-SP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Multimodal voice conversion using deep bottleneck features and deep canonical correlation analysis 
Sub Title (in English)  
Keyword(1) Voice conversion  
Keyword(2) multi-modal  
Keyword(3) audio-visual  
Keyword(4) cross-modal  
Keyword(5) deep learning  
Keyword(6) bottleneck feature  
Keyword(7) canonical component analysis  
Keyword(8)  
1st Author's Name Satoshi Tamura  
1st Author's Affiliation Gifu University (Gifu Univ.)
2nd Author's Name Kento Horio  
2nd Author's Affiliation Gifu University (Gifu Univ.)
3rd Author's Name Hajime Endo  
3rd Author's Affiliation Gifu University (Gifu Univ.)
4th Author's Name Satoru Hayamizu  
4th Author's Affiliation Gifu University (Gifu Univ.)
5th Author's Name Tomoki Toda  
5th Author's Affiliation Nagoya University (Nagoya Univ.)
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Speaker Author-1 
Date Time 2018-06-28 15:10:00 
Presentation Time 30 minutes 
Registration for SP 
Paper # PRMU2018-24, SP2018-4 
Volume (vol) vol.118 
Number (no) no.111(PRMU), no.112(SP) 
Page pp.13-18 
#Pages
Date of Issue 2018-06-21 (PRMU, SP) 


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