@inproceedings{chiou-etal-2022-mandarin,
    title = "{M}andarin-{E}nglish Code-Switching Speech Recognition System for Specific Domain",
    author = "Chiou, Chung-Pu  and
      Lin, Hou-An  and
      Chen, Chia-Ping",
    editor = "Chang, Yung-Chun  and
      Huang, Yi-Chin",
    booktitle = "Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)",
    month = nov,
    year = "2022",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.rocling-1.25/",
    pages = "200--204",
    language = "zho",
    abstract = "This paper will introduce the use of Automatic Speech Recognition (ASR) technology to process speech content with specific domain. We will use the Conformer end-to-end model as the system architecture, and use pure Chinese data for initial training. Next, use the transfer learning technology to fine-tune the system with Mandarin-English code-switching data. Finally, use the Mandarin-English code-switching data with a specific domain makes the final fine-tuning of the model so that it can achieve a certain effect on speech recognition in a specific domain. Experiments with different fine-tuning methods reduce the final error rate from 82.0{\%} to 34.8{\%}."
}