Depression is a major mental health concern that can be reflected through subtle changes in speech patterns, prosody, and vocal characteristics. In low-resource and multilingual settings, depression detection from speech may become particularly more challenging. In this work, we present our system for the Shared Task on Depression Detection from Malayalam and Tamil. We explored both handcrafted acoustic features (MFCC) and pretrained speech representations (Wav2Vec2) for depression detection, along with a simple fusion strategy to examine their complementary strengths. Our observations showed that Wav2Vec2 generalized better for Malayalam, whereas for Tamil, a validation-tuned probability fusion performed best. The final system achieved macro-F1 scores of 99.5% for Malayalam and 88.6% for Tamil, securing 3rd place in both tasks.

Abusive comment detection in low-resource languages poses significant challenges, particularly when targeting gender-based abuse on social media platforms. This work presents our system for ’Abusive Tamil text targeting women on social media’ at DravidianLangTech@ACL 2026. We introduce nine handcrafted lexicon features integrated with pretrained multilingual transformer embeddings and evaluate their effectiveness in classifying Tamil online comments as abusive or non-abusive. To better understand their impact, we compare model performance with and without these lexical attributes across multiple transformer architectures. Our best-performing model, XLM-RoBERTa-Large, achieved a macro F1-score of 81.71%, securing 15th rank in the competition. The findings indicate that larger multilingual models generalize more effectively to unseen data compared to smaller domain-specific models, while the addition of lexical features yields only mild gains.

Detecting psychological defense mechanisms in supportive conversations is essential for assisting mental health practitioners. Natural language processing techniques are increasingly integral to such systems, enabling automated classification of defense levels to better understand help-seeker behavior and resistance patterns. In PsyDefDetect at BioNLP 2026, we address the task of nine-class defense level classification on the PSYDEFCONV corpus. We propose a three-stage pipeline combining LLM-based dialogue summarization, domain-specific transformer fine-tuning, and rule-based ensemble prediction. Additionally, we evaluate three mental health domain-specific transformers (Mental-BERT, Mental-RoBERTa, Mental-XLNet) alongside fine-tuned LLMs (Qwen3-4B, Qwen3-1.7B, Mistral-7B under different input conditions. Experimental results on the released test-set gold labels show that our ensemble approach achieves the best performance, reaching 34.69% macro F1 and surpassing the baseline by 4.69 percentage points. On the official PsyDefDetect Leaderboard 1 (labels 1–8), the submitted system achieved a Macro-F1 score of 23.46%, ranking 15th out of 21 teams, while on Leaderboard 2 (labels 0–8), it achieved 30.04%, securing 14th place. These findings demonstrate that domain-specific transformers substantially outperform generic LLM fine-tuning on this specialized clinical task.