SBIR/STTR Research Award

Automatic Spoken Language Recognition for Machine Foreign Language Translation (MFLT)

Award identifier 5c1cc02b6a583830c80c1d3f743e5f8eb4fd2e558ba6d795810a9dbc172587ecAwarding agency Department of Defense · Army

Award amount

$150,000

Program and phase

SBIR · Phase I

Award year

2012

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 Defense
Branch
Army
Recipient company
VOCI TECHNOLOGIES INCORPORATED
Proposal award date
2012-04-23
Contract end date
2013-07-05
UEI
WRL3N9JB4AS9
Solicitation number
2012.1
Topic code
A12-031

Research abstract

Voci Technologies Incorporated (Voci) is partnering with Bhiksha Raj, a Voci advisor and Professor at Carnegie Mellon University, to prototype an Automated Spoken Language Recognition System (ASLRS) specifically designed to enhance the utility of Speech to Speech Machine Foreign Language Translation (MFLT) systems for the warfighter. The proposed ASLRS will be developed by tailoring Voci"s existing language identification technologies to fulfill the requirements of an efficient MFLT preprocessor. To provide best in class accuracy, a combination of techniques will be used including acoustic UBM-GMM (Universal-Background-Model/Gaussian-Mixture-Model), and PPRLM-SVM (Parallel-Phonetic-Recognition-Language-Model/Support-Vector-Machine). To meet the ASLRS real-time requirements a groundbreaking, patent pending, multi-language phonetic dictionary capable of doing phonetic recognition in all 6 target languages in a single pass will be utilized. An open-set based solution will be provided so that the ASLRS will recognize when an out of domain language is spoken. To ensure that the resulting ASLRS is generally applicable, it will be architected to be an open system that is easily integratable with existing MFLT solutions. To ensure that the system provides reliable results, even in noisy environment, the system will incorporate noise robust features. Finally, to address the shortcomings of existing solutions in real-world field conditions, the Voci Team will integrate an online incremental learning capability into the ASLRS so that it can adapt to different accents and noise conditions that exist during field use. At the end of Phase I the Team will demonstrate the prototype ASLR system. We believe the final implementation will yield a revolutionary new ASLR capability.

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

6 SBIR/STTR records share this UEI (2012–2016). Reported award amounts total $1,692,874. Reported amounts are not federal obligations.

Phases: Phase II, Phase I

Agencies: Department of Defense

Agency intelligence

Department of Defense

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.