American Sign Language Video to Text Translation
This work addresses communication barriers for individuals with hearing difficulties, but it is incremental as it builds on a recently published study.
The study replicated and attempted to improve a recent sign language to text translation model, finding that performance is significantly affected by optimizers, activation functions, and label smoothing, with evaluation using BLEU and rBLEU metrics.
Sign language to text is a crucial technology that can break down communication barriers for individuals with hearing difficulties. We replicate and try to improve on a recently published study. We evaluate models using BLEU and rBLEU metrics to ensure translation quality. During our ablation study, we found that the model's performance is significantly influenced by optimizers, activation functions, and label smoothing. Further research aims to refine visual feature capturing, enhance decoder utilization, and integrate pre-trained decoders for better translation outcomes. Our source code is available to facilitate replication of our results and encourage future research.