Paper Abstract and Keywords |
Presentation |
2022-06-17 13:00
A Study of Speech Recognition Result Correction Using BERT for Speech Translation Tadashi Ogura, Masakiyo Fujimoto, Peng Shen, Xugang Lu, Hisashi Kawai (NICT) SP2022-4 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Speech translation (ST) technology consists of automatic speech recognition (ASR) and machine translation technologies. Since ASR is the first module to be processed in ST, improving ASR performance is a critical factor in ST. However, it is difficult to solve this problem by pursuing only improvement of ASR performance, and error correction processing for ASR results is strongly required. Therefore, in this paper, we propose an error correction method for ASR results to improve ST performance using the state-of-the-art huge scale language model, BERT. The proposed method realizes context-aware error correction for ASR results, and successfully improves the accuracy of ST by reducing misunderstandings of “meaning" and "intention" of utterances. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
BERT / Spech Translation / Error Correction / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 81, SP2022-4, pp. 10-13, June 2022. |
Paper # |
SP2022-4 |
Date of Issue |
2022-06-10 (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) |
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SP2022-4 |
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