SBIR/STTR Research Award

Smartphone Machine Learning Speech Enhancement System for Hearing Aid Devices for the Underserved and Rural Populations

Award identifier 77330298ec0fa6966ddf5ef2fb5678274c00ca8eeb54cd87e9534d8640e88479Awarding agency Department of Health and Human Services · National Institutes of Health

Award amount

$295,825

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
National Institutes of Health
Recipient company
APPLIED UNIVERSAL DYNAMICS, CORP
Proposal award date
2024-08-15
Contract end date
2025-07-31
UEI
FX3HURWMGC21
Solicitation number
MD23-003
Topic code
NIA

Research abstract

Abstract This proposal responds directly to the purpose of the RFA-MD-23-003 SBIR Funding Opportunity Announcement (FOA). Nearly 44 million Americans experience hearing loss, but as much as 86% of adults who could benefit from hearing aid devices (HAD) do not use them. Untreated hearing loss is almost twice as prevalent in rural areas. More than 46 million people live in rural America, but these communities face significant barriers and delays in accessing care and experience a lack of providers for audiology services. The average retail price of HADs was $4,700, making it prohibitively expensive for the rural and minority populations who have lower incomes. Hearing loss is also highly prevalent in black and Latino/Hispanic communities and growing, but older black adults were 58% and Mexican American older adults were 78% less likely to use HADs, due to reasons of high cost and lack of insurance coverage. Aside from cost, concerns about comfort, effectiveness and satisfaction are the other significant barriers to HAD usage. The FDA made over-the-counter (OTC) HADs a reality in 2022, creating a new hearing option for many adults with perceived mild to moderate hearing loss. The introduction of OTC HADs is set to increase accessibility and affordability of hearing care for underserved populations. A common complaint of HAD users is the HAD inability to reduce ambient background noise completely, instead, the background noise is amplified along with the human voice. Speech enhancement algorithms continue to be developed and the most advanced found on high-end costly HAD platforms. This project will develop machine learning (ML) based smartphone software with a Graphical User Interface (GUI), to bring these advanced speech enhancement and noise reduction algorithms to the OTC HAD users. With smartphone assistive software and the predictive power of ML, our proposed solution to reduce background noise and enhance speech, will significantly improve OTC HAD accessibility, affordability, and acceptance, thus benefitting the rural and minority populations the most.

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

16 SBIR/STTR records share this UEI (2018–2025). Reported award amounts total $15,434,751. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

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.