@inproceedings{karunanidhi-arumugam-2026-chmod-777-dravidianlangtech,
    title = "{CHMOD}{\_}777@{D}ravidian{L}ang{T}ech 2026: {T}amil-Adapted Whisper and {MMS} for Dialect Speech Recognition and Classification",
    author = "Karunanidhi, Arunaggiri Pandian  and
      Arumugam, Prabalakshmi",
    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-1.24/",
    pages = "186--190",
    ISBN = "979-8-89176-401-9",
    abstract = "This paper describes Team CHMOD{\_}777{'}s system for the DravidianLangTech@ACL 2026 shared task on Tamil dialect speech recognition and classification. The task comprises two subtasks: classifying Tamil speech into four regional dialects (Northern, Southern, Western, Central) and transcribing dialectal Tamil speech to text. For dialect classification, we fine-tune MMS-1b-all with Focal Loss and weighted sampling, achieving 83.04 Macro F1 on the development set (5th out of 11 teams on the test set). For speech recognition, we fine-tune a Tamil-specific Whisper model (763M parameters), achieving 53.72 WER on the development set and 49.75 on the official test set, ranking 1st out of 13 teams. Our key finding is that domain-specific pre-training significantly outperforms larger general-purpose models: Tamil Whisper (763M) beats Whisper-large-v3 (1.5B) by 8 WER points despite having half the parameters."
}