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Paper Abstract and Keywords
Presentation 2020-03-02 13:00
Data augmentation for ASR system by using locally time-reversed speech -- Temporal inversion of feature sequence --
Takanori Ashihara, Tomohiro Tanaka, Takafumi Moriya, Ryo Masumura, Yusuke Shinohara, Makio Kashino (NTT) EA2019-110 SIP2019-112 SP2019-59
Abstract (in Japanese) (See Japanese page) 
(in English) Data augmentation is one of the techniques to mitigate overfitting and improve robustness against several acoustic variabilities for the ASR system. This approach is to create artificially augmented data by adding certain types of transformations that maintain the class label for acquiring generalization ability. In this paper, we treat an auditory illusion as the acoustic transformation for the data generation. The auditory illusions related to speech signals have been proposed variously. Among them, we examine a locally time-reversed speech for data augmentation, especially. In our previous research, we proposed temporal reversal processing on a raw waveform directly. In contrast, we propose a method that processes the inversion on a feature sequence in this paper. Instead of the inversion of the raw waveform, the augmentation is able to eliminate the generation of an additional waveform, and thus enables online data creation during training. We applied the augmentation approach on the End-to-End automatic speech recognition task and evaluated the model compared with the baseline model by using CSJ corpus. As a result, the relative performance improvement of 8.4% was observed relative to the baseline.
Keyword (in Japanese) (See Japanese page) 
(in English) automatic speech recognition / End-to-End / locally time-reversed speech / data augmentation / auditory illusion / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 441, SP2019-59, pp. 53-58, March 2020.
Paper # SP2019-59 
Date of Issue 2020-02-24 (EA, SIP, SP) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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 EA2019-110 SIP2019-112 SP2019-59

Conference Information
Committee SP EA SIP  
Conference Date 2020-03-02 - 2020-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Industry Support Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2020-03-SP-EA-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Data augmentation for ASR system by using locally time-reversed speech 
Sub Title (in English) Temporal inversion of feature sequence 
Keyword(1) automatic speech recognition  
Keyword(2) End-to-End  
Keyword(3) locally time-reversed speech  
Keyword(4) data augmentation  
Keyword(5) auditory illusion  
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1st Author's Name Takanori Ashihara  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Tomohiro Tanaka  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Takafumi Moriya  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
4th Author's Name Ryo Masumura  
4th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
5th Author's Name Yusuke Shinohara  
5th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
6th Author's Name Makio Kashino  
6th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2020-03-02 13:00:00 
Presentation Time 90 minutes 
Registration for SP 
Paper # EA2019-110, SIP2019-112, SP2019-59 
Volume (vol) vol.119 
Number (no) no.439(EA), no.440(SIP), no.441(SP) 
Page pp.53-58 
#Pages
Date of Issue 2020-02-24 (EA, SIP, SP) 


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