Published Paper Details:

This project aims to develop an advanced communication system called SignSpeak, designed to bridge the gap between spoken language and sign language, thereby promoting greater inclusivity for individuals with hearing and speech impairments in public interactions and official functions. In many real-world scenarios, the absence of sign language interpreters poses a significant communication barrier, often excluding hearing- impaired individuals from accessing essential information. The existing solutions involve stitching together video clips, based on the input text. These solutions are not scalable and moreover the actions rigid and lacks real time feedback loop. SignSpeak addresses this challenge by leveraging Automatic Speech Recognition and Natural Language Processing to capture and semantically interpret real-time speech input. The processed text is then translated into sign language using a Sign Language Generation model, which is rendered through an expressive virtual avatar capable of conveying sign language gestures accurately and naturally. By automating the entire speech-to-sign language conversion pipeline, the system eliminates the dependency on human interpreters, ensuring scalability, cost-effectiveness, and consistent accuracy across diverse environments such as government offices, hospitals, educational institutions, and public events. Ultimately, SignSpeak offers a robust, real-time, and scalable solution that enhances communication accessibility, promotes digital inclusivity, and empowers the hearing-impaired community by enabling equal participation in society through improved access to spoken information.