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Paper Abstract and Keywords
Presentation 2014-12-16 13:30
[Poster Presentation] speech selection and environmental adaptation for asynchronous speech recording based on deep neural network
Bo Ren, Longbiao Wang (Nagaoka Univ. of Tech.), Atsuhiko Kai (Shizuoka Univ.) SP2014-121
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a robust distant-talking speech recognition system with asynchronous speech recording. This is implemented by combining automatic asynchronous speech (microphone or mobile terminal) selection and environmental adaptation with deep neural network. Although applications using mobile terminals have attracted increasing attention, there are few studies that focus on distant-talking speech recognition with asynchronous mobile terminals. For the system proposed in this paper, by using bottleneck features (BNFs) from a deep neural network (DNN) rather than the conventional mel-frequency cesptral coefficients (MFCCs), we adopted the state-of-the-art deep neural network acoustic model, environmental adaptation and automatic asynchronous speech selection. The proposed method was evaluated by using a reverberant WSJCAM0 corpus, which was emitted by a loudspeaker and recorded in a meeting room with multiple speakers by far-field multiple mobile terminals. By invoking the bottleneck features and DNN acoustic model with automatic asynchronous speech selection and environmental adaptation, the average Word Error Rate (WER) was reduced from 55.32% of the baseline system to 19.38%, i.e. the relative error reduction rate was 64.97%.
Keyword (in Japanese) (See Japanese page) 
(in English) distant-talking speech recognition / tandem DNN / hybrid DNN / model adaptation / asynchronous speech / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 365, SP2014-121, pp. 129-134, Dec. 2014.
Paper # SP2014-121 
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 English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) speech selection and environmental adaptation for asynchronous speech recording based on deep neural network 
Sub Title (in English)  
Keyword(1) distant-talking speech recognition  
Keyword(2) tandem DNN  
Keyword(3) hybrid DNN  
Keyword(4) model adaptation  
Keyword(5) asynchronous speech  
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1st Author's Name Bo Ren  
1st Author's Affiliation Nagaoka University of Technology (Nagaoka Univ. of Tech.)
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
Date Time 2014-12-16 13:30:00 
Presentation Time 90 
Registration for SP 
Paper # IEICE-SP2014-121 
Volume (vol) IEICE-114 
Number (no) no.365 
Page pp.129-134 
#Pages IEICE-6 
Date of Issue IEICE-SP-2014-12-08 


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