Sign Language Translation

Sign language translation (SLT) aims to automatically convert sign language videos into spoken language text, bridging communication gaps between deaf and hearing individuals. Current research heavily utilizes transformer-based neural networks, often incorporating multi-stream approaches to process hand gestures, facial expressions, and body movements simultaneously, and exploring techniques like contrastive learning to improve feature discrimination. This field is significant for its potential to improve accessibility for deaf and hard-of-hearing communities and is driving advancements in multimodal machine learning, particularly in handling continuous, dynamic data streams and addressing data scarcity challenges through techniques like data augmentation and self-supervised learning.