@inproceedings{cheng-wu-2025-speech,
    title = "Speech Recognition for Low-resource Languages: A Comparative Study on {H}akka {H}an Characters and {R}omanization",
    author = "Cheng, Yu-Hsiang  and
      Wu, Yi-Syuan",
    editor = "Chang, Kai-Wei  and
      Lu, Ke-Han  and
      Yang, Chih-Kai  and
      Tam, Zhi-Rui  and
      Chang, Wen-Yu  and
      Wang, Chung-Che",
    booktitle = "Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)",
    month = nov,
    year = "2025",
    address = "National Taiwan University, Taipei City, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.rocling-main.49/",
    pages = "435--440",
    ISBN = "979-8-89176-379-1",
    abstract = "This study focuses on speech recognition for low-resource languages, with Hakka as the case study. Since there is currently a lack of dedicated speech models for Taiwanese Southern Min, Hakka, and indigenous languages, we adopt OpenAI Whisper-Medium as the base model and apply Low-Rank Adaptation (LoRA) for fine-tuning. Two models with different output forms were developed: a Hakka character-based model and a Hakka phonetic-based model. The experimental dataset contains approximately 80 hours of speech, covering the Dapu and Zhao{'}an dialects, and the models were evaluated using Character Error Rate (CER) and Word Error Rate (WER)."
}