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| Presentation | 2022-12-01 15:20 Domain and language adaptation of large-scale pretrained model for speech recognition of low-resource language Kak Soky (Kyoto University), Sheng Li (NICT), Chenhui Chu, Tatsuya Kawahara (Kyoto University)  NLC2022-17 SP2022-37 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The self-supervised learning (SSL) models are effective for automatic speech recognition (ASR). Due to the huge parameter size, it usually requires about 10 hours of data for finetuning ASR. However, such size of ASR training data is unavailable for some low-resource languages. Moreover, the SSL pre-trained models were originally trained using European languages; they thus might not be well-adapted to other domains or languages. To bare those challenges, We propose a two-step adaptation method: (1) domain adaptation, which uses in-domain multi-lingual datasets to finetune the pre-trained model, and (2) language adaptation, which finetunes the same language datasets but different domains. Then, we investigate the effectiveness of adapting only one hour of target-labeled data for the ASR task. The experiment using the Extraordinary Chambers in the Courts of Cambodia dataset shows that first conducting domain adaption and then language adaption is the most effective method for reducing the CER of the baseline by 6.15% and 7.75% of the test and validation sets, respectively. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Speech recognition / domain adaptation / language adaptation / low-resource / Khmer language / wav2vec2.0-based / self-supervised learning / large-scale pre-trained model | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 288, SP2022-37, pp. 45-49, Nov. 2022. | |
| Paper # | SP2022-37 | |
| Date of Issue | 2022-11-22 (NLC, 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 | NLC2022-17 SP2022-37 |
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| 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 | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Domain and language adaptation of large-scale pretrained model for speech recognition of low-resource language |
| Sub Title (in English) | |
| Keyword(1) | Speech recognition |
| Keyword(2) | domain adaptation |
| Keyword(3) | language adaptation |
| Keyword(4) | low-resource |
| Keyword(5) | Khmer language |
| Keyword(6) | wav2vec2.0-based |
| Keyword(7) | self-supervised learning |
| Keyword(8) | large-scale pre-trained model |
| 1st Author's Name | Kak Soky |
| 1st Author's Affiliation | Kyoto University (Kyoto University) |
| 2nd Author's Name | Sheng Li |
| 2nd Author's Affiliation | National Institute of Information and Communications Technology (NICT) |
| 3rd Author's Name | Chenhui Chu |
| 3rd Author's Affiliation | Kyoto University (Kyoto University) |
| 4th Author's Name | Tatsuya Kawahara |
| 4th Author's Affiliation | Kyoto University (Kyoto University) |
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| Speaker | Author-1 |
| Date Time | 2022-12-01 15:20:00 |
| Presentation Time | 30 minutes |
| Registration for | SP |
| Paper # | NLC2022-17, SP2022-37 |
| Volume (vol) | vol.122 |
| Number (no) | no.287(NLC), no.288(SP) |
| Page | pp.45-49 |
| #Pages | 5 |
| Date of Issue | 2022-11-22 (NLC, SP) |
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