@inproceedings{b-etal-2026-findings,
    title = "Findings in {T}amil Dialect Speech Recognition and Classification",
    author = "B, Bharathi  and
      Chakravarthi, Bharathi Raja  and
      Chinnan, Shunmuga Priya Muthusamy  and
      S, Saranya  and
      S, Suhasini",
    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-1.9/",
    pages = "71--79",
    ISBN = "979-8-89176-401-9",
    abstract = "As part of DravidianLangTech-2026, we provide a overview of Shared Task on Dialect-based Speech Recognition and Classification in Tamil. Creating reliable system for Tamil dialect identification from audio signals and dialect-aware Automatic Speech Recognition (ASR) is the main goal of the joint work. Dialect-based Tamil Speech Recognition and Tamil Dialect Classification from Speech are the two subtasks that make up the task. 5,134 audio recordings in four Tamil dialects: Southern, Northern, Western, and Central-spanning 9 hours and 22 minutes make up the training dataset. There are 579 audio samples in the test set, totaling almost two hours in length. The shared task involved 17 teams in total. For speech recognition and dialect classification, the top-performing system obtained a Word Error Rate (WER) of 0.51 and a macro F1-score of 0.79, respectively. The findings emphasize the difficulties in understanding Tamil speech due to dialectal diversity and set solid foundations for further study on low-resource dialect-aware ASR systems."
}