| IEICE Technical Committee Submission System Conference Paper's Information |
Online Proceedings [Sign in] Tech. Rep. Archives |
| Go Top Page | Go Previous | [Japanese] / [English] |
| 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 | |
| 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 | 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 |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 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) |
| 5th Author's Name | |
| 5th Author's Affiliation | () |
| 6th Author's Name | |
| 6th Author's Affiliation | () |
| 7th Author's Name | |
| 7th Author's Affiliation | () |
| 8th Author's Name | |
| 8th Author's Affiliation | () |
| 9th Author's Name | |
| 9th Author's Affiliation | () |
| 10th Author's Name | |
| 10th Author's Affiliation | () |
| 11th Author's Name | |
| 11th Author's Affiliation | () |
| 12th Author's Name | |
| 12th Author's Affiliation | () |
| 13th Author's Name | |
| 13th Author's Affiliation | () |
| 14th Author's Name | |
| 14th Author's Affiliation | () |
| 15th Author's Name | |
| 15th Author's Affiliation | () |
| 16th Author's Name | |
| 16th Author's Affiliation | () |
| 17th Author's Name | |
| 17th Author's Affiliation | () |
| 18th Author's Name | |
| 18th Author's Affiliation | () |
| 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 |
| #Pages | 6 |
| Date of Issue | 2020-02-24 (EA, SIP, SP) |
[Return to Top Page]