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Paper Abstract and Keywords
Presentation 2014-12-15 10:45
Investigation of Deep Neural Network and Cross-adaptation for Voice Activity Detection in Meeting Speech
Akihiro Nakadani (Shizuoka Univ.), Longbiao Wang (Nagaoka Univ. of Tech.), Atsuhiko Kai (Shizuoka Univ.) SP2014-107
Abstract (in Japanese) (See Japanese page) 
(in English) In voice activity detection(VAD), performance largely decreases under the influence of noise and reverberation. In this paper, we focus on a VAD technique with deep neural network(DNN) framework and propose the environmental adaptation methods of the VAD model. As for the unsupervised adaptation of such discriminative models, utilizing erroneous identification result as target signal often reproduces an error and degrades the performance. As for unsupervised adaptation techniques in ASR systems, cross adaptation method using different types of models, has been proposed. Our cross-adaptation method improves the VAD performance by using recognition output of GMM and SVM which are different from DNN in terms of error tendency for unsupervised adaptation and achieves a robust VAD system capable of adapting the noisy and reverberant environment.
Keyword (in Japanese) (See Japanese page) 
(in English) Voice activity detection(VAD) / Deep neural network(DNN) / Cross-adaptation / Noisy and reverberant speech / Environmental adaptation / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 365, SP2014-107, pp. 19-24, Dec. 2014.
Paper # SP2014-107 
Date of Issue 2014-12-08 (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 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) Investigation of Deep Neural Network and Cross-adaptation for Voice Activity Detection in Meeting Speech 
Sub Title (in English)  
Keyword(1) Voice activity detection(VAD)  
Keyword(2) Deep neural network(DNN)  
Keyword(3) Cross-adaptation  
Keyword(4) Noisy and reverberant speech  
Keyword(5) Environmental adaptation  
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1st Author's Name Akihiro Nakadani  
1st Author's Affiliation Shizuoka University (Shizuoka Univ.)
2nd Author's Name Longbiao Wang  
2nd Author's Affiliation Nagaoka University of Technology (Nagaoka Univ. of Tech.)
3rd Author's Name Atsuhiko Kai  
3rd Author's Affiliation Shizuoka University (Shizuoka Univ.)
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Speaker Author-1 
Date Time 2014-12-15 10:45:00 
Presentation Time 25 minutes 
Registration for SP 
Paper # SP2014-107 
Volume (vol) vol.114 
Number (no) no.365 
Page pp.19-24 
#Pages
Date of Issue 2014-12-08 (SP) 


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