@inproceedings{ba38b021c00d450bbbf119c67e251160,
title = "TRCA: Text Restoration for Chinese ASR with BERT",
abstract = "Text restoration plays a vital role in Chinese automatic speech recognition (ASR), which includes punctuation prediction and error correction. However, there are two inevitable challenges for this task. On the one hand, there are no public dataset and model for Chinese punctuation prediction. On the other hand, current text restoration methods for automatic speech recognition only focus on Chinese error correction instead of combining with Chinese punctuation prediction task. To address these problems, a BERT-based text restoration method called TRCA is proposed for Chinese ASR consisting of a Chinese punctuation prediction model and a Chinese error correction model. Experiments demonstrate that the proposed TRCA method outperforms state-of-the-art methods for both punctuation prediction and error correction tasks, among which the proposed TRCA improves the average accuracy to 98% in Chinese punctuation prediction.",
keywords = "BERT, Chinese ASR text restoration, Chinese error correction, Chinese punctuation prediction",
author = "Xing Wu and Yuan Zhang and Jianjia Wang and Yike Guo",
note = "Publisher Copyright: {\textcopyright} 2022 The authors and IOS Press. All rights reserved.; 21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022 ; Conference date: 20-09-2022 Through 22-09-2022",
year = "2022",
month = sep,
day = "14",
doi = "10.3233/FAIA220295",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "661--668",
editor = "Hamido Fujita and Yutaka Watanobe and Takuya Azumi",
booktitle = "New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022",
}