This paper presents the **Wise** system for the shared task on dialect-based speech processing in Tamil, addressing two subtasks: **(1) four-way dialect region classification** (Northern, Southern, Western, Central), and **(2) dialectal Tamil ASR**. All audio is preprocessed using loudness normalization followed by neural denoising to ensure consistent audio quality for downstream models. For classification, we experiment with different model variants combining multilingual and Tamil-pretrained **Wav2Vec2** backbones with five temporal pooling strategies under frozen and partial fine-tuning settings. Our best configuration, i.e., learned attentive pooling with partial fine-tuning and a differentially trained MLP head, achieves a macro F1 of **0.79**, securing **1st place** with a margin of **0.26** points. For ASR, we propose two novel **dialect-conditioned Whisper** architectures—residual injection and cross-attention—that inject dialect embeddings from the trained classifier into the ASR pipeline. In addition, we evaluate a vanilla Whisper-Tamil fine-tuned baseline. The best model achieved a **WER of 0.90**, securing **8th place** in the shared task.

Political memes in Tamil and Malayalampresent unique multimodal challenges for automated under-standing, combining visual context with code-mixed, cultur-ally grounded text. We present SYNAPSE, our system forthe DravidianLangTech@ACL 2026 shared task on multi-levelpolitical meme classification. The task requires hierarchicalclassification of memes along two levels: Level 1 identifies thepolitical stance (Support/Praise vs. Troll/Oppose), and Level 2identifies the target (individual person vs. party). Our approachfine-tunes the Qwen3-VL-2B-Instruct vision-language modelusing parameter-efficient LoRA adapters on task-specific mul-timodal data, with structured output prompting for hierarchi-cal label prediction. We report results for both Tamil andMalayalam subtracks. For Malayalam, our system achievesa Level 1 F1 of 0.9200 and Level 2 F1 of 0.4256 (Avg-F1:0.6728, Rank 5). For Tamil, our system achieves a Level 1 F1of 0.7840 and Level 2 F1 of 0.4885 (Avg-F1: 0.6362, Rank 14).

Social media platforms like X (twitter) have gained popularity for political debates and election campaigns in the last decade. This creates the need to moderate and understand the sentiments of the tweets in order to understand the state of digital campaigns. This paper focuses on political sentiment classification of Tamil X (Twitter) comments which proves to be challenging because of the presence of informal expressions, code-switching, and limited annotated datasets. This study focuses on categorizing them into seven classes: substantiated, sarcastic, opinionated, positive, negative, neutral, and none of the above. This paper proposes a solution to Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments - DravidianLangTech@NAACL 2025 shared task, the solution incorporates IndicBERTv2-MLM-Back-Translation model and TF-IDF vectors into a custom model. Further we explore the use of preprocessing techniques to enrich hashtags and emojis with their context. Our approach achieved Rank 1 with a macro F1 average of 0.38 in the shared task.