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Paper Abstract and Keywords
Presentation 2022-12-01 15:50
ASR model adaptation to target domain with large-scale audio data without transcription
Takahiro Kinouchi, Daiki Mori (TUT), Ogawa Atsunori (NTT), Norihide Kitaoka (TUT) NLC2022-18 SP2022-38
Abstract (in Japanese) (See Japanese page) 
(in English) Nowadays, speech recognition is used in various services and businesses thanks to the advent of high-performance models such as the Transformer speech recognition model. However, to train our high-performance speech recognition model from scratch, we need a large amount of speech data and its transcribed text data. It is both time-consuming and economically difficult for us to prepare these data on our own. On the other hand, it is relatively easy to prepare only the speech data of the target domain. Therefore, in this study, we integrate the wav2vec 2.0 model, which is pre-trained only with a large amount of target domain speech data, and the decoder module of the Transformer speech recognition model, which is pre-trained with a large amount of out-of-domain corpus, to create an speech recognition model that is comparatively applicable to the target domain. The purpose of this study is to create a speech recognition model for the target domain in an environment where the training data (speech data and its transcribed text data) of the target domain does not exist.
Keyword (in Japanese) (See Japanese page) 
(in English) wav2vec 2.0 / domain adaptation / end-to-end speech recognition / Encoder-Decoder model / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 288, SP2022-38, pp. 50-53, Nov. 2022.
Paper # SP2022-38 
Date of Issue 2022-11-22 (NLC, SP) 
ISSN 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)
Download PDF NLC2022-18 SP2022-38

Conference Information
Committee NLC IPSJ-NL SP IPSJ-SLP  
Conference Date 2022-11-29 - 2022-12-01 
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 2022-11-NLC-NL-SP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) ASR model adaptation to target domain with large-scale audio data without transcription 
Sub Title (in English)  
Keyword(1) wav2vec 2.0  
Keyword(2) domain adaptation  
Keyword(3) end-to-end speech recognition  
Keyword(4) Encoder-Decoder model  
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Keyword(6)  
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1st Author's Name Takahiro Kinouchi  
1st Author's Affiliation Toyohashi University of Technology (TUT)
2nd Author's Name Daiki Mori  
2nd Author's Affiliation Toyohashi University of Technology (TUT)
3rd Author's Name Ogawa Atsunori  
3rd Author's Affiliation NIPPON TELEGRAPH AND TELEPHONE CORPORATION (NTT)
4th Author's Name Norihide Kitaoka  
4th Author's Affiliation Toyohashi University of Technology (TUT)
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Speaker Author-1 
Date Time 2022-12-01 15:50:00 
Presentation Time 20 minutes 
Registration for SP 
Paper # NLC2022-18, SP2022-38 
Volume (vol) vol.122 
Number (no) no.287(NLC), no.288(SP) 
Page pp.50-53 
#Pages 4 
Date of Issue 2022-11-22 (NLC, SP) 

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