The rise of social media in Bangladesh has increased abusive and hateful content, which is difficult to detect due to the informal nature of Bangla and limited resources. The BLP 2025 shared task addressed this challenge with Subtask 1A (multi-label abuse categories) and Subtask 1B (target identification). We propose a parameter-efficient model using a frozen BanglaBERT backbone with hierarchical attention to capture token level importance across hidden layers. Context vectors are aggregated for classification, combining syntactic and semantic features. On Subtask 1A, our frozen model achieved a micro-F1 of 0.7178, surpassing the baseline of 0.7100, while the unfrozen variant scored 0.7149. Our submissions ranked 15th (Subtask 1A) and 12th (Subtask 1B), showing that layer-wise attention with a frozen backbone can effectively detect abusive Bangla text.

Conventional research on speech recognition modeling relies on the canonical form for most low-resource languages while automatic speech recognition (ASR) for regional dialects is treated as a fine-tuning task. To investigate the effects of dialectal variations on ASR we develop a 78-hour annotated Bengali Speech-to-Text (STT) corpus named Ben-10. Investigation from linguistic and data-driven perspectives shows that speech foundation models struggle heavily in regional dialect ASR, both in zero-shot and fine-tuned settings. We observe that all deep learning methods struggle to model speech data under dialectal variations, but dialect specific model training alleviates the issue. Our dataset also serves as a out-of-distribution (OOD) resource for ASR modeling under constrained resources in ASR algorithms. The dataset and code developed for this project are publicly available.