@inproceedings{k-b-2026-aitamildialect,
    title = "{AIT}amil{D}ialect@{D}ravidian{L}ang{T}ech 2026: Zero-Shot Whisper and {W}av2{V}ec2 Embedding-Based {T}amil Speech Recognition and Dialect Classification.",
    author = "K, Varalakshmi  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-1.17/",
    pages = "148--152",
    ISBN = "979-8-89176-401-9",
    abstract = "Low-resource languages pose significant challenges for speech technology due to linguistic variation and limited annotated resources. One such language is Tamil, which is a morphologically rich language with significant dialectal variations, which makes Automatic Speech Recognition (ASR) and dialect classification a challenging task. In this article, we introduce a shared-task system for handling Speech Processing in Tamil Language covering both ASR and Dialect classification. We use the Whisper Large-v3 multilingual model in a zero-shot setting without task-specific fine-tuning. For dialect classification, we employ a pre-trained Wav2Vec2 model to extract acoustic features and mean and standard deviation pooling to create utterance-level representations, with an XGBoost model trained for four-way prediction of dialects. Experiments on 579 Tamil speech samples resulted in a word error rate (WER) of 0.61, highlighting the difficulty of the dialectal ASR problem in low- resource setting. The dialect classification system obtained an accuracy of 0.49 and a macro F1 score of 0.41, and there was a certain amount of confusion between the dialect classes. The proposed system is purely based on the standard pretrained models without adaptation, but has produced a benchmark that can be replicated in the multilingual speech representation evaluation of Tamil low-resource scenarios. The results also indicate the need for additional strategies to improve the robustness of the model and stronger baseline models and improved methods for embedding-based dialect classification for future research."
}