Title: BdSL-SPOTER: A Transformer-Based Framework for Bengali Sign Language Recognition with Cultural Adaptation
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Bengali Sign Language, SPOTER, Transformer Architecture, Sign Language Recognition, Accessibility Technology, Deep Learning

Abstract: Bengali Sign Language (BdSL) recognition is under-resourced and faces challenges from limited data, signer variability, and cultural differences in signing conventions. We introduce BdSL-SPOTER, a compact pose-based transformer that combines BdSL-specific signing-space normalization, motion-aware attention biasing, and a community-derived prototype regularizer to reduce intra-class variance and improve generalization. Evaluated on the BdSLW60 corpus (9,307 videos; signer-independent 5-fold CV), BdSL-SPOTER achieves 94.2% ± 1.8% Top-1 accuracy while remaining computationally efficient (≈847K parameters, 127 FPS on A100). We provide formal definitions, ablations, signer-disjoint evaluation, and ethical/community validation to support reproducibility and responsible deployment.

TL;DR: a culturally-aware pose transformer that uses signing-space normalization, motion-aware attention, and prototype regularization to boost BdSLW60 Top-1 from ~82% to 94.2% while staying compact and fast.