AITamilDialect@DravidianLangTech 2026: Zero-Shot Whisper and Wav2Vec2 Embedding-Based Tamil Speech Recognition and Dialect Classification.

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.