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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
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 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)  
Keyword(1) Speech Recognition  
Keyword(2) Acoustic Model  
Keyword(3) Noise Robustness  
Keyword(4) Deep Neural Network  
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  
4th Author's Affiliation 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 
Date of Issue 2017-02-22 (EA, SIP, SP) 

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