Deaf Response

Deaf response research focuses on improving communication accessibility for deaf and hard-of-hearing individuals, primarily through advancements in sign language recognition and translation, as well as personalized speech and sound recognition systems. Current research employs various machine learning models, including LSTMs, transformers (like BERT and T5), and deep learning architectures for object detection, often leveraging transfer learning and data augmentation techniques to address data scarcity. This work aims to bridge communication gaps by developing more accurate and inclusive technologies, impacting both the scientific understanding of human-computer interaction and the daily lives of deaf and hard-of-hearing communities.