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Paper Abstract and Keywords
Presentation 2020-03-02 13:00
Japanese dialect speech classification using sequence-to-one neural networks
Ryo Imaizumi (TMU), Ryo Masumura (NTT), Sayaka Shiota, Hitoshi Kiya (TMU) EA2019-108 SIP2019-110 SP2019-57
Abstract (in Japanese) (See Japanese page) 
(in English) The language specific to a certain region is called a dialect, and the task of identifying which dialect the input speech is called dialect identification.
Many of the speech recognition models are made up of standard languages, and it is known that the recognition performance is greatly reduced when using the model to recognize speech including dialects.
One way to solve this problem is to use dialect information for learning and prepare a model that can identify both standard and dialects.
Dialect identification to decide which recognizer to use for input speech is important.
Also, if the accuracy of dialect identification is very high, improving the speech recognition system can be expected by optimizing the language model from the dialect-specific information, so improving the accuracy of the dialect identification model is an important task.
In this study, we used a neural network as a model for dialect identification, and used a framework called End-to-End, which has been widely used in recent years.
In the experiment, the performance of the discrimination model for dialects in six regions, Aomori, Hiroshima, Kumamoto, Nagoya, Sapporo, and Sendai, was investigated and analyzed using a sequential classification neural network with various parameters.
Keyword (in Japanese) (See Japanese page) 
(in English) Japanese dialect identification / sequence-to-one neural network / LSTM / BLSTM / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 441, SP2019-57, pp. 41-46, March 2020.
Paper # SP2019-57 
Date of Issue 2020-02-24 (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 EA2019-108 SIP2019-110 SP2019-57

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) Japanese dialect speech classification using sequence-to-one neural networks 
Sub Title (in English)  
Keyword(1) Japanese dialect identification  
Keyword(2) sequence-to-one neural network  
Keyword(3) LSTM  
Keyword(4) BLSTM  
1st Author's Name Ryo Imaizumi  
1st Author's Affiliation Tokyo Metropolitan University (TMU)
2nd Author's Name Ryo Masumura  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Sayaka Shiota  
3rd Author's Affiliation Tokyo Metropolitan University (TMU)
4th Author's Name Hitoshi Kiya  
4th Author's Affiliation Tokyo Metropolitan University (TMU)
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Speaker Author-1 
Date Time 2020-03-02 13:00:00 
Presentation Time 90 minutes 
Registration for SP 
Paper # EA2019-108, SIP2019-110, SP2019-57 
Volume (vol) vol.119 
Number (no) no.439(EA), no.440(SIP), no.441(SP) 
Page pp.41-46 
Date of Issue 2020-02-24 (EA, SIP, SP) 

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