@inproceedings{teng-etal-2025-whisper,
    title = "Whisper Finetuning For {H}akka Recognition in Low Resource",
    author = "Teng, Min Han  and
      Chen, Ci Dao  and
      Lin, You Ting  and
      Huang, Bing Jhih",
    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/dashboard/2025.rocling-main.52/",
    pages = "450--453",
    ISBN = "979-8-89176-379-1",
    abstract = "We study automatic speech recognition (ASR) for Hakka, a low-resource language with substantial dialectal variation. Focusing on Zhaoan and Dapu, we fine-tune Whisper using Low-Rank Adaptation (LoRA) and apply data augmentation to mitigate data scarcity. Experiments show that LoRA combined with augmentation substantially improves cross-dialect recognition while maintaining parameter efficiency. Our results demonstrate the potential of lightweight adaptation to extend large-scale ASR systems to underrepresented languages, supporting the preservation of Hakka speech and orthography."
}