@inproceedings{gao-etal-2025-wenzhou,
    title = "Wenzhou Dialect Speech to {M}andarin Text Conversion",
    author = "Gao, Zhipeng  and
      Tamura, Akihiro  and
      Kato, Tsuneo",
    editor = "Ojha, Atul Kr.  and
      Liu, Chao-hong  and
      Vylomova, Ekaterina  and
      Pirinen, Flammie  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Zhao, Xiaobing",
    booktitle = "Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, U.S.A.",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.loresmt-1.5/",
    pages = "36--43",
    ISBN = "979-8-89176-230-5",
    abstract = "The Wenzhou dialect is a Chinese dialect that is significantly distinct from Mandarin, the official language of China. It is among the most complex Chinese dialects and is nearly incomprehensible to people from regions such as Northern China, thereby creating substantial communication barriers. Therefore, the conversion between the Wenzhou dialect and Mandarin is essential to facilitate communication between Wenzhou dialect speakers and those from other Chinese regions. However, as a low-resource language, the Wenzhou dialect lacks publicly available datasets, and such conversion technologies have not been extensively researched. Thus, in this study, we create a parallel dataset containing Wenzhou dialect speech and the corresponding Mandarin text and build benchmark models for Wenzhou dialect speech-to-Mandarin text conversion. In particular, we fine-tune two self-supervised learning-based pretrained models, that is, TeleSpeech-ASR1.0 and Wav2Vec2-XLS-R, with our training dataset and report their performance on our test dataset as baselines for future research."
}