@inproceedings{dipto-etal-2025-asr,
    title = "Are {ASR} foundation models generalized enough to capture features of regional dialects for low-resource languages?",
    author = "Dipto, Tawsif Tashwar  and
      Azmol Hossain  and
      Faruque, Rubayet Sabbir  and
      Hassan, Md. Rezuwan  and
      Fatema, Kanij  and
      Shome, Tanmoy  and
      Naswan, Ruwad  and
      Zihad, Md.Foriduzzaman  and
      Anam, Mohaymen Ul  and
      Tasnim, Nazia  and
      Mahmud, Hasan  and
      Hasan, Md Kamrul  and
      Shawon, Md. Mehedi Hasan  and
      Sadeque, Farig  and
      Reasat, Tahsin",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-short.17/",
    pages = "178--188",
    ISBN = "979-8-89176-299-2",
    abstract = "Conventional research on speech recognition modeling relies on the canonical form for most low-resource languages while automatic speech recognition (ASR) for regional dialects is treated as a fine-tuning task. To investigate the effects of dialectal variations on ASR we develop a 78-hour annotated Bengali Speech-to-Text (STT) corpus named Ben-10. Investigation from linguistic and data-driven perspectives shows that speech foundation models struggle heavily in regional dialect ASR, both in zero-shot and fine-tuned settings. We observe that all deep learning methods struggle to model speech data under dialectal variations, but dialect specific model training alleviates the issue. Our dataset also serves as a out-of-distribution (OOD) resource for ASR modeling under constrained resources in ASR algorithms. The dataset and code developed for this project are publicly available."
}