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Paper Abstract and Keywords
Presentation 2020-03-03 09:00
Semi-supervised Self-produced Speech Enhancement and Suppression Based on Joint Source Modeling of Air- and Body-conducted Signals Using Variational Autoencoder
Shogo Seki, Moe Takada, Kazuya Takeda, Tomoki Toda (Nagoya Univ.) EA2019-140 SIP2019-142 SP2019-89
Abstract (in Japanese) (See Japanese page) 
(in English) This paper proposes a semi-supervised method for enhancing and suppressing self-produced speech, using a variational autoencoder (VAE) to jointly model speech recorded with air- and body-conductive microphones. In speech enhancement and suppression for self-produced speech, body-conducted signals can be used as an acoustical clue since they are robust against external noise and include self-produced speech predominantly.We have previously developed a semi-supervised method taking an improved source modeling approach called joint source modeling, which can capture the nonlinear correspondence of air- and body-conducted signals using non-negative matrix factorization (NMF). This allows air-conducted enhanced and suppressed signals to be prevented from contaminating by characteristics of body-conducted signals. However, one drawback of our previous method is that the joint source modeling depends on the representation power of NMF, which falls into limited performances. To overcome this issue, this paper proposes a joint source modeling of air- and body-conducted signals using a VAE, which has shown to represent source signals more accurately than NMF. Experimental results revealed that the proposed method outperformed baseline methods.
Keyword (in Japanese) (See Japanese page) 
(in English) Semi-supervised speech enhancement and suppression / Air- and body-conducted signal / Joint source modeling / Variational autoencoder (VAE) / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 441, SP2019-89, pp. 225-230, March 2020.
Paper # SP2019-89 
Date of Issue 2020-02-24 (EA, SIP, 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)
Download PDF EA2019-140 SIP2019-142 SP2019-89

Conference Information
Committee SP EA SIP  
Conference Date 2020-03-02 - 2020-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Industry Support Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2020-03-SP-EA-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Semi-supervised Self-produced Speech Enhancement and Suppression Based on Joint Source Modeling of Air- and Body-conducted Signals Using Variational Autoencoder 
Sub Title (in English)  
Keyword(1) Semi-supervised speech enhancement and suppression  
Keyword(2) Air- and body-conducted signal  
Keyword(3) Joint source modeling  
Keyword(4) Variational autoencoder (VAE)  
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1st Author's Name Shogo Seki  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Moe Takada  
2nd Author's Affiliation Nagoya University (Nagoya Univ.)
3rd Author's Name Kazuya Takeda  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
4th Author's Name Tomoki Toda  
4th Author's Affiliation Nagoya University (Nagoya Univ.)
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Speaker Author-1 
Date Time 2020-03-03 09:00:00 
Presentation Time 90 minutes 
Registration for SP 
Paper # EA2019-140, SIP2019-142, SP2019-89 
Volume (vol) vol.119 
Number (no) no.439(EA), no.440(SIP), no.441(SP) 
Page pp.225-230 
#Pages 6 
Date of Issue 2020-02-24 (EA, SIP, SP) 

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