SBIR/STTR Research Award

The Sign Language Detection for Videoconferencing (SLDVC) project will provide a unique feature which automatically detects sign language in videoconferencing and highlights that person's window.

Award identifier 27d6222a2d309004c89b652097b741a1427ab2c0e40114d998b6c46155c0e661Awarding agency Department of Health and Human Services · Administration for Community Living

Award amount

$100,000

Program and phase

SBIR · Phase I

Award year

2024

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
Department of Health and Human Services
Branch
Administration for Community Living
Recipient company
GOVOBO LLC
Proposal award date
Not reported
Contract end date
Not reported
UEI
FZYYR6UYLVK9
Solicitation number
HHS-2024-ACL-NIDILRR-BISA-0069
Topic code
NIDILRR

Research abstract

GoVoBo, LLC, a company formed in September 2022, collaborates with Gallaudet University and AppTek to enhance videoconferencing experiences for the Deaf and Hard of Hearing (DHH) community through innovative Artificial Intelligence (AI) technologies. The project addresses significant challenges faced by DHH individuals in using mainstream videoconferencing platforms. The spotlighting feature in videoconferencing platforms is a critical tool for enhancing DHH communication accessibility. It enhances visibility and focus by prominently displaying the current speaker or signer, crucial for clear comprehension in group settings. To date, spotlighting for sign language must be handled manually. Automating it would foster better engagement and participation from DHH individuals in virtual meetings, webinars, or educational settings, making it an indispensable element in digital communication platforms. This project focuses on developing an automated spotlighting feature that intelligently detects sign language turn-taking. This advancement aims to provide more accessible and equitable communication for the DHH community, by making sign language a first-class citizen in turn-taking, co-equal with spoken language. This initiative aligns with the accessibility principles advocated by the World Wide Web Consortium and addresses improvements asked for by deaf and hard of hearing consumers. The project leverages recent advancements in AI and Computer Vision, particularly in Sign Language Detection. It plays a crucial role in differentiating sign language from non-signing movements (gestures or hand signals), thereby reducing false positives and enhancing the user experience in videoconferencing. By accurately tracking and highlighting sign language, it ensures that the videoconferencing system’s spotlight feature is activated appropriately, focusing on signers when actual signing occurs. The new feature in GoVoBo's application will connect with existing videoconferencing platforms in a platform-agnostic manner.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

2 SBIR/STTR records share this UEI (2024–2025). Reported award amounts total $387,500. Reported amounts are not federal obligations.

Phases: Phase II, Phase I

Agencies: Department of Health and Human Services

Agency intelligence

Department of Health and Human Services

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.