American Sign Language

American Sign Language (ASL) research focuses on developing technologies to improve communication and accessibility for the Deaf community. Current efforts concentrate on building accurate and unbiased sign language recognition and translation systems, employing deep learning models like transformers, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), often incorporating multimodal data (hand gestures, facial expressions) and leveraging large-scale datasets like YouTube-ASL. These advancements aim to create real-time sign-to-text and text-to-sign systems, improving accessibility to information and services, and fostering greater inclusivity. Furthermore, research explores the linguistic structure of ASL itself, investigating factors like communicative efficiency and phonological properties to enhance model performance and understanding of the language.