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Paper Abstract and Keywords
Presentation 2022-03-08 13:25
A Study on Sign Recognition Using Deep Learning -- Comparison between CTC and Conformer --
Hikaru Isogai, Tsutomu Kimura (NIT, Toyota College), Kanda Kazuyuki (National Museum of Ethnology) WIT2021-48
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, our purpose is to recognize signs using machine learning. In order to take into account the transition motions that occur in a sign sentence, machine learning adopts the sign sentences as training data, and a trained model is created. We experimented two models: one that incorporates Connectionist Temporal Classification (CTC) which is a method used in speech recognition, and the other is a conformer model that uses a transformer used in natural language processing. As the result, the recognition rate for the entire test data was about 74% by the CTC method and about 32% by the Conformer method. However, the recognition results of the Conformer method showed a phenomenon as over-learning, and we estimated that it might worked properly. We will improve the Conformer method and will investigate a new algorithm that combines the Transformer with CTC.
Keyword (in Japanese) (See Japanese page) 
(in English) Sign Recognition / Deep Learning / Connectionist Temporal Classification / Transformer / Conformer / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 418, WIT2021-48, pp. 29-34, March 2022.
Paper # WIT2021-48 
Date of Issue 2022-03-01 (WIT) 
ISSN 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)
Download PDF WIT2021-48

Conference Information
Committee WIT IPSJ-AAC  
Conference Date 2022-03-08 - 2022-03-09 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To WIT 
Conference Code 2022-03-WIT-AAC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Sign Recognition Using Deep Learning 
Sub Title (in English) Comparison between CTC and Conformer 
Keyword(1) Sign Recognition  
Keyword(2) Deep Learning  
Keyword(3) Connectionist Temporal Classification  
Keyword(4) Transformer  
Keyword(5) Conformer  
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Keyword(7)  
Keyword(8)  
1st Author's Name Hikaru Isogai  
1st Author's Affiliation National Institute of Technology, Toyota College (NIT, Toyota College)
2nd Author's Name Tsutomu Kimura  
2nd Author's Affiliation National Institute of Technology, Toyota College (NIT, Toyota College)
3rd Author's Name Kanda Kazuyuki  
3rd Author's Affiliation National Museum of Ethnology (National Museum of Ethnology)
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Speaker Author-1 
Date Time 2022-03-08 13:25:00 
Presentation Time 25 minutes 
Registration for WIT 
Paper # WIT2021-48 
Volume (vol) vol.121 
Number (no) no.418 
Page pp.29-34 
#Pages 6 
Date of Issue 2022-03-01 (WIT) 

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