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Paper Abstract and Keywords
Presentation 2023-06-24 13:50
Automatic speech recognition model simultaneously recognizes linguistic information and verbal/non-verbal phenomena
Nagito Shione, Yukoh Wakabayashi, Norihide Kitaoka (TUT) SP2023-22
Abstract (in Japanese) (See Japanese page) 
(in English) Although speech recognition technology has advanced in recent years, most of them recognize only linguistic information and cannot recognize verbal/non-verbal (VNV) phenomena. In this study, we propose a speech recognition model that simultaneously recognizes various types of VNV phenomena and linguistic information. The VNV phenomena to be recognized are filler, laughter, rising intonation in questions, end of speech, word errors, word restarts, small speech, speech related to the flow of conversation, and speech in dialects and foreign languages. In addition, we investigated the effect of the position of tags indicating VNV on speech recognition. The experimental results demonstrated that the best speech recognition model for the CEJC database was the one in which the VNV phenomenon tag was annotated before the linguistic information in the transcribed text. We also showed that the accuracy of speech recognition is improved by recognizing VNV phenomena.
Keyword (in Japanese) (See Japanese page) 
(in English) End-to-End automatic speech recognition / Encoder-Decoder model / verbal phenomena / non-verbal phenomena / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 88, SP2023-22, pp. 109-113, June 2023.
Paper # SP2023-22 
Date of Issue 2023-06-16 (SP) 
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 SP2023-22

Conference Information
Committee SP IPSJ-MUS IPSJ-SLP  
Conference Date 2023-06-23 - 2023-06-24 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2023-06-SP-MUS-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Automatic speech recognition model simultaneously recognizes linguistic information and verbal/non-verbal phenomena 
Sub Title (in English)  
Keyword(1) End-to-End automatic speech recognition  
Keyword(2) Encoder-Decoder model  
Keyword(3) verbal phenomena  
Keyword(4) non-verbal phenomena  
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1st Author's Name Nagito Shione  
1st Author's Affiliation Toyohashi University Of Technology (TUT)
2nd Author's Name Yukoh Wakabayashi  
2nd Author's Affiliation Toyohashi University Of Technology (TUT)
3rd Author's Name Norihide Kitaoka  
3rd Author's Affiliation Toyohashi University Of Technology (TUT)
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Speaker Author-1 
Date Time 2023-06-24 13:50:00 
Presentation Time 140 minutes 
Registration for SP 
Paper # SP2023-22 
Volume (vol) vol.123 
Number (no) no.88 
Page pp.109-113 
#Pages 5 
Date of Issue 2023-06-16 (SP) 

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