International Journal of Computational Linguistics & Chinese Language Processing, Volume 26, Number 1, June 2021

7438

Authors

nan

Abstract

This paper describes the NTNU ASR system participating in the Formosa Speech Recognition Challenge 2020 (FSR-2020) supported by the Formosa Speech in the Wild project (FSW). FSR-2020 aims at fostering the development of Taiwanese speech recognition. Apart from the issues on tonal and dialectical variations of the Taiwanese language, speech artificially contaminated with different types of real-world noise also has to be dealt with in the final test stage; all of these make FSR-2020 much more challenging than before. To work around the under-resourced issue, the main technical aspects of our ASR system include various deep learning techniques, such as transfer learning, semi-supervised learning, front-end speech enhancement and model ensemble, as well as data cleansing and data augmentation conducted on the training data. With the best configuration, our system obtains 13.1 % syllable error rate (SER) on the final-test set, achieving the first place among all participating systems on Track 3. Keywords: Formosa Speech Recognition Challenge, Deep Learning, Transfer Learning, Semi-supervised Training 1.

Keywords

nan

Full Content

International Journal of Computational Linguistics & Chinese Language Processing Aims and Scope International Journal of Computational Linguistics and Chinese Language Processing (IJCLCLP) is an international journal published by the Association for Computational Linguistics and Chinese Language Processing (ACLCLP). This journal was founded in August 1996 and is published four issues per year since 2005. This journal covers all aspects related to computational linguistics and speech/text processing of all natural languages. Possible topics for manuscript submitted to the journal include, but are not limited to: • Computational Linguistics • Natural Language Processing • Machine Translation • Language Generation • Language Learning • Speech Analysis/Synthesis • Speech Recognition/Understanding • Spoken Dialog Systems • Information Retrieval and Extraction • Web Information Extraction/Mining • Corpus Linguistics • Multilingual/Cross-lingual Language Processing Membership & Subscriptions If you are interested in joining ACLCLP, please see appendix for further information. Copyright © The Association for Computational Linguistics and Chinese Language Processing International Journal of Computational Linguistics and Chinese Language Processing is published four issues per volume by the Association for Computational Linguistics and Chinese Language Processing. Responsibility for the contents rests upon the authors and not upon ACLCLP, or its members. Copyright by the Association for Computational Linguistics and Chinese Language Processing. All rights reserved. No part of this journal may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical photocopying, recording or otherwise, without prior permission in writing form from the Editor-in Chief. Cover Calligraphy by Professor Ching-Chun Hsieh, founding president of ACLCLP Text excerpted and compiled from ancient Chinese classics, dating back to 700 B.C. This calligraphy honors the interaction and influence between text and language International Journal of Computational Linguistics and Chinese Language Processing vol. 26, no. 1, June 2021 Contents Papers The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 2020…………………………………….... Fu-An Chao, Tien-Hong Lo, Shi-Yan Weng, Shih-Hsuan Chiu, Yao-Ting Sung and Berlin Chen 1 NSYSU-MITLab 團隊於福爾摩沙語音辨識競賽 2020 之語音辨 識系統 [NSYSU-MITLab Speech Recognition System for Formosa Speech Recognition Challenge 2020]........................…... 林洪邦(Hung-Pang Lin),陳嘉平(Chia-Ping Chen) 17 2020 福爾摩沙臺語語音辨識比賽之初步實驗 [A Preliminary Study of Formosa Speech Recognition Challenge 2020 – Taiwanese ASR]...……………………..……………………..…... 余福浩(Fu-Hao Yu),盧克函(Ke-Han Lu),王繹崴(Yi-Wei Wang), 張維哲(Wei-Zhe Chang),黃偉愷(Wei-Kai Huang), 陳冠宇(Kuan-Yu Chen) 33 Textual Relations with Conjunctive Adverbials in English Writing by Chinese Speakers: A corpus-based Approach………... Tung-Yu Kao and Li-Mei Chen 43 中文新聞文本之宣傳手法標記與分析 [The Analysis and Annotation of Propaganda Techniques in Chinese News Texts]…. 施孟賢(Meng-Hsien Shih),段人鳳(Ren-feng Duann), 鍾曉芳(Siaw-Fong Chung) 79 The Association for Computational Linguistics and Chinese Language Processing Computational Linguistics and Chinese Language Processing Vol. 26, No. 1, June 2021, pp. 1-16 1  The Association for Computational Linguistics and Chinese Language Processing The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 2020 Fu-An Chao, Tien-Hong Lo, Shi-Yan Weng, Shih-Hsuan Chiu, Yao-Ting Sung, and Berlin Chen Abstract This paper describes the NTNU ASR system participating in the Formosa Speech Recognition Challenge 2020 (FSR-2020) supported by the Formosa Speech in the Wild project (FSW). FSR-2020 aims at fostering the development of Taiwanese speech recognition. Apart from the …

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Labeled Text

This paper describes the NTNU ASR system participating in the Formosa Speech Recognition Challenge 2020 (FSR-2020) supported by the Formosa Speech in the Wild project (FSW). FSR-2020 aims at fostering the development of Taiwanese speech recognition. Apart from the issues on tonal and dialectical variations of the Taiwanese language, speech artificially contaminated with different types of real-world noise also has to be dealt with in the final test stage; all of these make FSR-2020 much more challenging than before. To work around the under-resourced issue, the main technical aspects of our ASR system include various deep learning techniques, such as transfer learning, semi-supervised learning, front-end speech enhancement and model ensemble, as well as data cleansing and data augmentation conducted on the training data. With the best configuration, our system obtains 13.1 % syllable error rate (SER) on the final-test set, achieving the first place among all participating systems on Track 3. Keywords: Formosa Speech Recognition Challenge, Deep Learning, Transfer Learning, Semi-supervised Training 1.

Publication Details

Paper ID:
7438
Year:
2021
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan