An Approach of Translating Speech to Sign Language using Machine Learning techniques

This paper presents a real-time speech-to-sign language translation system aiming to bridge communication gaps between the hearing and deaf communities. Leveraging advanced machine learning and computer vision techniques, the system translates spoken language into sign language gestures in real-time. The abstract highlights the system's innovation in converting verbal communication to visual sign language, enhancing inclusivity and accessibility for the deaf and hard-of-hearing individuals. The paper discusses the underlying architecture, including the integration of speech recognition, natural language processing, and sign language animation modules. The system's evaluation highlights its accuracy, speed, and effectiveness in enhancing cross-modal communication, breaking barriers between hearing and deaf individuals for greater integration and understanding.