@inproceedings{s-b-priya-b-2026-tamilvoicelab,
    title = "{T}amil{V}oice{L}ab@{D}ravidian{L}ang{T}ech 2026: Investigating Whisper {T}amil Large-v2 for Dialectal {T}amil Speech Recognition",
    author = "S.b.priya  and
      B, Bharathi",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Rajiakodi, Saranya  and
      Navaneethakrishnan, Subalalitha  and
      Chinnappa, Dhivya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      Rajalakshmi, Ratnavel",
    booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
    month = jul,
    year = "2026",
    address = "Underline (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.63/",
    pages = "397--402",
    ISBN = "979-8-89176-401-9",
    abstract = "Automatic Speech Recognition (ASR) for languages rich in dialects and those with limited resources presents significant challenges due to the variations in pronunciation and vocabulary across different regions. This study offers a baseline evaluation of the Whisper Tamil Large-v2 model without fine-tuning for the Tamil Dialect Speech Recognition shared task. The focus is on the ASR subtask, utilizing dialectal Tamil speech recordings gathered from various regional dialects within Tamil Nadu. The pretrained Whisper Tamil Large-v2 model was assessed directly, without any supplementary fine-tuning or domain adaptation. A total of 579 dialect speech samples were used for experimentation, with performance evaluated based on Word Error Rate (WER). The model recorded a WER of 0.71, indicating that even robust multilingual pretrained models encounter challenges in dialect-rich and low-resource environments. These findings underscore the necessity for dialect-aware adaptation and the importance of balanced dialect training data to develop effective Tamil ASR systems."
}