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 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) |
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SP2014-107 |
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) |
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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 |
6 |
Date of Issue |
2014-12-08 (SP) |