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Paper Abstract and Keywords
Presentation 2019-03-15 13:30
[Poster Presentation] Data augmentation using multiple databases for end-to-end dysarthric speech recognition
Yuki Takashima, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) EA2018-156 SIP2018-162 SP2018-118
Abstract (in Japanese) (See Japanese page) 
(in English) We present in this paper an end-to-end speech recognition system for a Japanese person with an articulation disorder resulting from athetoid cerebral palsy. The movements of such speakers are limited by their athetoid symptoms, and their utterances are often unstable or unclear, which makes it difficult for them to communicate. Therefore, the performance of automatic speech recognition (ASR) systems for people with an articulation disorder degrades significantly. Recently, deep learning approaches to speech recognition have seen much progress. These techniques require a large amount of training data, however, the amount of data from people with articulation disorder is limited due to their athetoid symptoms. This paper proposes a data augmentation method using not only the speech data of a Japanese person with an articulation disorder but also the speech data of a physically unimpaired Japanese person and a non-Japanese person with an articulation disorder. We employ an end-to-end ASR model based on the listen, attend and spell (LAS) model which has an acoustic module and a language module. In our proposed model, the acoustic module is shared between people with dysarthria, and a language module is assigned to each language regardless of dysarthria. The effectiveness of this approach was confirmed through word-recognition experiments where our proposed method outperformed a method based on the conventional LAS model.
Keyword (in Japanese) (See Japanese page) 
(in English) Speech recognition / Multilingual / Assistive technology / End-to-end model / Dysarthria / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 497, SP2018-118, pp. 335-340, March 2019.
Paper # SP2018-118 
Date of Issue 2019-03-07 (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 EA2018-156 SIP2018-162 SP2018-118

Conference Information
Committee EA SIP SP  
Conference Date 2019-03-14 - 2019-03-15 
Place (in Japanese) (See Japanese page) 
Place (in English) i+Land nagasaki (Nagasaki-shi) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Engineering/Electro Acoustics, Signal Processing, Speech, and Related Topics 
Paper Information
Registration To SP 
Conference Code 2019-03-EA-SIP-SP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Data augmentation using multiple databases for end-to-end dysarthric speech recognition 
Sub Title (in English)  
Keyword(1) Speech recognition  
Keyword(2) Multilingual  
Keyword(3) Assistive technology  
Keyword(4) End-to-end model  
Keyword(5) Dysarthria  
1st Author's Name Yuki Takashima  
1st Author's Affiliation Kobe University (Kobe Univ.)
2nd Author's Name Tetsuya Takiguchi  
2nd Author's Affiliation Kobe University (Kobe Univ.)
3rd Author's Name Yasuo Ariki  
3rd Author's Affiliation Kobe University (Kobe Univ.)
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Speaker Author-1 
Date Time 2019-03-15 13:30:00 
Presentation Time 90 minutes 
Registration for SP 
Paper # EA2018-156, SIP2018-162, SP2018-118 
Volume (vol) vol.118 
Number (no) no.495(EA), no.496(SIP), no.497(SP) 
Page pp.335-340 
Date of Issue 2019-03-07 (EA, SIP, SP) 

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