Data Quality Issues in Multilingual Speech Datasets: The Need for Sociolinguistic Awareness and Proactive Language Planning
Mingfei Lau, Qian Chen, Yeming Fang, Tingting Xu, Tongzhou Chen, Pavel Golik
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.
BibTeX
@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://aclanthology.org/2025.acl-long.370/",
doi = "10.18653/v1/2025.acl-long.370",
pages = "7466--7492",
ISBN = "979-8-89176-251-0"
}