Paper Abstract and Keywords |
Presentation |
2017-03-02 09:00
[Poster Presentation]
Study of branch selecting DNN acoustic model for robustness to environmental variation Takafumi Moriya, Taichi Asami, Yoshikazu Yamaguchi, Yushi Aono (NTT) EA2016-131 SIP2016-186 SP2016-126 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
The performance of speech recognition tasks can be significantly improved by the use of deep neural networks (DNN). Speech recognition system is demanded to high recognition performance with the increase in use scene of itself. However, it needs to prepare acoustic models corresponding to each environment to obtain the best recognition results. Also, to train and make each acoustic model takes a lot of costs that contain preparation of a large amount of training data and computational time for training. The goal of this paper is to obtain an acoustic model that can adapt each environmental speech data and output high recognition results. We propose DNN architecture that is diverged and converged at input, and hidden or output layer respectively. The each pass of diverged DNN architecture is trained by using each environmental speech data, so it has a role for robustness to environmental variation. Compared to no diverged DNN architecture, our proposed DNN architecture improves character accuracy. Its relative error rate is 9.6%. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Speech Recognition / Acoustic Model / Noise Robustness / Deep Neural Network / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 116, no. 477, SP2016-126, pp. 277-282, March 2017. |
Paper # |
SP2016-126 |
Date of Issue |
2017-02-22 (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) |
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EA2016-131 SIP2016-186 SP2016-126 |
Conference Information |
Committee |
SP SIP EA |
Conference Date |
2017-03-01 - 2017-03-02 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Okinawa Industry Support Center |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Speech, Engineering/Electro Acoustics, Signal Processing, and Related Topics |
Paper Information |
Registration To |
SP |
Conference Code |
2017-03-SP-SIP-EA |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Study of branch selecting DNN acoustic model for robustness to environmental variation |
Sub Title (in English) |
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Keyword(1) |
Speech Recognition |
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Acoustic Model |
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Noise Robustness |
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Deep Neural Network |
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1st Author's Name |
Takafumi Moriya |
1st Author's Affiliation |
Nippon Telegraph and Telephone Corporation (NTT) |
2nd Author's Name |
Taichi Asami |
2nd Author's Affiliation |
Nippon Telegraph and Telephone Corporation (NTT) |
3rd Author's Name |
Yoshikazu Yamaguchi |
3rd Author's Affiliation |
Nippon Telegraph and Telephone Corporation (NTT) |
4th Author's Name |
Yushi Aono |
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Nippon Telegraph and Telephone Corporation (NTT) |
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Speaker |
Author-1 |
Date Time |
2017-03-02 09:00:00 |
Presentation Time |
90 minutes |
Registration for |
SP |
Paper # |
EA2016-131, SIP2016-186, SP2016-126 |
Volume (vol) |
vol.116 |
Number (no) |
no.475(EA), no.476(SIP), no.477(SP) |
Page |
pp.277-282 |
#Pages |
6 |
Date of Issue |
2017-02-22 (EA, SIP, SP) |
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