@inproceedings{yang-etal-2025-gigaspeech,
    title = "{G}iga{S}peech 2: An Evolving, Large-Scale and Multi-domain {ASR} Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement",
    author = "Yang, Yifan  and
      Song, Zheshu  and
      Zhuo, Jianheng  and
      Cui, Mingyu  and
      Li, Jinpeng  and
      Yang, Bo  and
      Du, Yexing  and
      Ma, Ziyang  and
      Liu, Xunying  and
      Wang, Ziyuan  and
      Li, Ke  and
      Fan, Shuai  and
      Yu, Kai  and
      Zhang, Wei-Qiang  and
      Chen, Guoguo  and
      Chen, Xie",
    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.135/",
    pages = "2673--2686",
    ISBN = "979-8-89176-251-0",
    abstract = "The evolution of speech technology has been spurred by the rapid increase in dataset sizes. Traditional speech models generally depend on a large amount of labeled training data, which is scarce for low-resource languages. This paper presents GigaSpeech 2, a large-scale, multi-domain, multilingual speech recognition corpus. It is designed for low-resource languages and does not rely on paired speech and text data. GigaSpeech 2 comprises about 30,000 hours of automatically transcribed speech, including Thai, Indonesian, and Vietnamese, gathered from unlabeled YouTube videos. We also introduce an automated pipeline for data crawling, transcription, and label refinement. Specifically, this pipeline involves Whisper for initial transcription, MMS for forced alignment, and multi-dimensional filtering for data quality assurance. A modified Noisy Student Training is developed to further refine flawed pseudo labels iteratively, thereby enhancing model performance. Experimental results on our manually transcribed evaluation set and two public test sets from Common Voice and FLEURS confirm our corpus{'}s high quality and broad applicability. Notably, ASR models trained on GigaSpeech 2 can reduce the word error rate for Thai, Indonesian, and Vietnamese on our challenging and realistic YouTube test set by 25{\%} to 40{\%} compared to Whisper large-v3, with merely 10{\%} model parameters. Furthermore, our ASR models trained on GigaSpeech 2 yield superior performance compared to commercial services. We hope that our newly introduced corpus and pipeline will open a new avenue for low-resource speech recognition and significantly facilitate research in this area."
}