SBIR Phase I: Real-Time Artificial Intelligence (AI) Bidirectional American Sign Language (ASL) Communication System

Sign-Speak Inc, Rochester NY

Investigators

Abstract

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to improve the communication between Deaf and Hard of Hearing (D/HH) individuals and the hearing community through automated sign language recognition. In the United States alone there are over 48 million D/HH individuals, who in total possess $87 billion in purchasing power. It appears businesses are not adequately serving this community, as is evidenced by the plethora of Americans with Disabilities Act (ADA) lawsuits against numerous companies. The proposed technology will provide plug-and-play software for organizations to improve their interactions with D/HH individuals. Businesses and governments will be able to interact with their D/HH employees, customers, or constituents when interpreters are unavailable. This technology can be integrated into a variety of platforms, from retail point-of-sale equipment to chatbots and video/teleconferencing systems. This Small Business Innovation Research (SBIR) Phase 1 project aims to develop technology to perform unconstrained sign language recognition and natural sign language production. Specifically, current methods to train language translation models are ill-equipped to handle the sign language domain due to the lack of training data within this domain. Additionally, all currently established methods (apart from motion capture, which is unscalable) for producing American Sign Language (ASL) result in stilted, unnatural signing from an avatar. This project will develop solutions to these issues within the domain of ASL via semi-supervised expert-augmented models and data augmentation techniques. Technical hurdles include the lack of models to handle high-dimensional low-resource language domains, and lack of sufficiently large datasets. Technical milestones include creating semi-supervised datasets, engineering data augmentation techniques, generating a natural signing avatar, and performing extensive usability testing. This project aims to produce a method for automatically interpreting between a low-resource sign language and English to improve accessibility and increase equity for the Deaf and Hard of Hearing communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.