Company
Portfolio Data
Back to Company SearchSIGN-SPEAK INC
Address
104 East Ave Ste 205Rochester, NY, 14604-2502
USA
UEI: KUL7Z2MRFNM3
Number of Employees: 1
HUBZone Owned: N/A
Woman Owned: N/A
Socially and Economically Disadvantaged: N/A
SBIR/STTR Involvement
Year of first award: 2023
1
Phase I Awards
1
Phase II Awards
100%
Conversion Rate
$256,000
Phase I Dollars
$1,250,000
Phase II Dollars
$1,506,000
Total Awarded
Awards
SBIR Phase II: Real-Time Artificial Intelligence (AI) Bidirectional American Sign Language (ASL) Communication System
Amount: $1,250,000 Topic: AI
The broader impact of this Small Business Innovation Research (SBIR) Phase II project will result from an advanced communication system that uses cutting-edge computer vision and machine learning to enhance communications for the Deaf and Hard of Hearing (DHH) community. By offering expanded communication options beyond traditional methods, the proposed technology seeks to reduce social isolation, foster greater independence, and promote full integration of DHH individuals into the U.S. economy and stimulate economic activity. The technology is expected to unlock opportunities for DHH individuals to access a range of services and platforms, fostering economic growth and enabling businesses and governments to achieve greater productivity. Societally, it will improve educational and employment outcomes for DHH individuals, contributing to increased economic participation. This Small Business Innovation Research (SBIR) Phase II project addresses the critical need for improved bi-directional communication between DHH and hearing people. The core intellectual challenge lies in developing robust artificial intelligence models given the inherent complexities of modeling a visual language and the scarcity of comprehensive data. Previous research has faced limitations, often resulting in models with inaccuracies, restricted domain applicability, or insufficient effectiveness. Building on successful Phase I efforts, which established technical viability of the proposed approach through novel dataset creation and data augmentation techniques, this project aims to overcome challenges associated with scaling the technology to deal with more complex real-world interactions. The proposed research will focus on refining the models developed in Phase I to create a comprehensive automatic two-way communication system. 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.
Tagged as:
SBIR
Phase II
2025
NSF
SBIR Phase I:Real-Time Artificial Intelligence (AI) Bidirectional American Sign Language (ASL) Communication System
Amount: $256,000 Topic: AI
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._x000D_ _x000D_ 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._x000D_ _x000D_ 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.
Tagged as:
SBIR
Phase I
2023
NSF