Acoustic and Textual Data Augmentation for Code-Switching Speech Recognition in Under-Resourced Language

I. Ting Hsieh, Chung Hsien Wu, Chun Huang Wang

研究成果: Conference contribution

摘要

Under-resourced and code-switching speech recognition have recently received research interest, resulting in several robust acoustic and language modeling approaches. As Taiwanese and Mandarin have been popularly and widely used in Taiwan, this paper aims to address the under-resourced and codeswitching issues. First, phone sharing between Taiwanese and Mandarin is employed for acoustic data augmentation to construct the acoustic models of Taiwanese speech recognizer. Regarding the lack of Taiwanese text corpus, this paper translates Mandarin corpus into Taiwanese corpus based on word-to-word translation. Moreover, additional translation rules for codeswitching text are manually designed. The augmented text corpus is then used for training the code-switching language models. In the experimental results, the word error rate for code-switching speech recognition was 26.02%, which was better than that trained by the pure Taiwanese corpus.

原文English
主出版物標題2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面302-307
頁數6
ISBN(電子)9789881476883
出版狀態Published - 2020 十二月 7
事件2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, New Zealand
持續時間: 2020 十二月 72020 十二月 10

出版系列

名字2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings

Conference

Conference2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020
國家New Zealand
城市Virtual, Auckland
期間20-12-0720-12-10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Signal Processing
  • Decision Sciences (miscellaneous)
  • Instrumentation

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