@inproceedings{lau-etal-2025-data,
    title = "Data Quality Issues in Multilingual Speech Datasets: The Need for Sociolinguistic Awareness and Proactive Language Planning",
    author = "Lau, Mingfei  and
      Chen, Qian  and
      Fang, Yeming  and
      Xu, Tingting  and
      Chen, Tongzhou  and
      Golik, Pavel",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.370/",
    pages = "7466--7492",
    ISBN = "979-8-89176-251-0",
    abstract = "Our quality audit for three widely used public multilingual speech datasets Mozilla Common Voice 17.0, FLEURS, and VoxPopuli shows that in some languages, these datasets suffer from significant quality issues. We believe addressing these issues will make these datasets more useful as evaluation sets, and improve downstream models. We divide these quality issues into two categories: micro-level and macro-level. We find that macro-level issues are more prevalent in less institutionalized, often under-resourced languages. We provide a case analysis of Taiwanese Southern Min (nan{\_}tw) that highlights the need for proactive language planning (e.g. orthography prescriptions, dialect boundary definition) and enhanced data quality control in the process of Automatic Speech Recognition (ASR) dataset creation. We conclude by proposing guidelines and recommendations to mitigate these issues in future dataset development, emphasizing the importance of sociolinguistic awareness in creating robust and reliable speech data resources."
}