@inproceedings{huang-etal-2025-applying,
    title = "Applying Whisper Fine-tuning and Branchformer to {H}akka Speech Recognition",
    author = "Huang, Yu-Sheng  and
      Hong, Wei-Cheng  and
      Chen, Xin-Yu  and
      Lin, Szu-Yin",
    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.50/",
    pages = "441--445",
    ISBN = "979-8-89176-379-1",
    abstract = "This study addresses the FSR 2025 Hakka speech recognition task by comparing two strategies: fine-tuning large pre-trained models and training from scratch. For character (Hanzi) recognition, we fine-tuned five different scales of the Whisper model, with large-v3-turbo achieving a 7.55{\%} CER on the test set. For Pinyin recognition, a Branchformer model was compared against a LoRA fine-tuned Whisper-small, yielding WERs of 4.7{\%} and 6.5{\%} on the test set, respectively. Speed perturbation was the primary method used for data augmentation in our pre-processing pipeline."
}