Tamil has a lot of internal variability, including the way it is used in casual conversations, code mixing, and phonetic differences in the way it is spoken in different regions, making it quite difficult to transcribe the spoken word and classify the dialects. In order to address these challenges, our paper presents the system developed by the CUET_InferX team for the Shared Task on Dialect Based Speech Recognition and Classification in Tamil, which was part of DravidianLangTech@ACL 2026. For Subtask 2 (ASR), our proposed system is based on a dual-architecture design that incorporates a fine-tuned Whisper-large-v3 model with Low-Rank Adaptation (LoRA) and a Wav2Vec2 XLSR-53 model, topped with a KenLM statistical language model for n-gram phonetic correction. Our ASR system resulted in a Word Error Rate (WER) of 0.54, which earned us 2nd position for Subtask 2. For Subtask 1 (Speech-Based Dialect Classification), our proposed system is based on a text-based weighted ensemble of IndicBERT, MuRIL, XLM-RoBERTa, and TamilBERT models, which is completely dependent on our ASR system’s transcription outputs. Our proposed system achieved a Macro F1 score of 0.22, which earned us 9th position for Subtask 1.

We present CUET_InferX, our submission to Subtask 1a (Spoken VQA, English-audio) of the ImageEval 2026 shared task on the AynVQA dataset. The task requires predicting the correct answer option solely from an image and a raw audio clip containing both a spoken question and three candidate options. We fine-tune Qwen2.5-Omni-3B with LoRA applied exclusively to the language reasoning backbone, keeping both audio and vision encoders frozen, under a single 16 GB Kaggle T4 GPU. Training uses a time-budgeted, class-stratified subsample of 2,055 examples. Our system achieves 91.20% test accuracy (Balanced Accuracy 87.72%, Macro-F1 80.50%), placing 3rd position, with gains of +7.20 accuracy and +15.95 Macro-F1 over the zero-shot base. We compare against a cascaded ASR→VLM alternative and zero-shot 3B/7B baselines, and explain with devtest evidence why we submitted the end-to-end fine-tuned model despite lower raw test accuracy than the cascade.