2323040
Project Grant
Overview
Grant Description
Sbir Phase I: A Language Learning App Based On Sound And Mouth Movements -The Broader/Commercial Impact Of This Small Business Innovation Research (Sbir) Phase I Project Is Advancing New Language Learning By Incorporating Facial And Lip Recognition Along With Sound Analysis.
This Visual Aspect Of Creating Sounds Is Vital For Mastering Pronunciation, One Of The Significant Hurdles Of Learning A Foreign Language And Even Improving A Native Language. Current Language Learning Methods Often Fall Short In Helping Learners Achieve Speaking Proficiency And Fail To Provide Real-Life Language Usage Experiences.
This Language Learning Platform Aims To Change This By Addressing The Growing Need For Multi-Language Proficiency In Workplaces And Academic Settings, Providing An Effective And Engaging Language Learning Experience.
Current Language Learning Methods And Apps Often Fail To Develop Speaking And Writing Proficiency, Focusing Instead On Memorization And Standardized Tests. This Language Trainer Addresses This Gap By Offering Insights Into The Science Of Speech Production.
By Combining Visual Cues Of Oral Shapes With Auditory Input, Learners Can Master Pronunciation, A Significant Challenge In Language Acquisition.
This Research Will Include Obtaining Near-Perfect Voice Files For Machine Learning Model Training, Signal Processing Of The Voice And Video Files, Development And Comparison Of Machine Learning Models, Data Visualization Development, Incorporation Into The Mobile Test Suite, And Preliminary Testing.
The Machine Learning Algorithms Will Use The Insights Extracted From Students' Voice Data To Provide Learners With Highly Targeted, Fine-Tuned Activities.
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.
- Subawards Are Not Planned For This Award.
This Visual Aspect Of Creating Sounds Is Vital For Mastering Pronunciation, One Of The Significant Hurdles Of Learning A Foreign Language And Even Improving A Native Language. Current Language Learning Methods Often Fall Short In Helping Learners Achieve Speaking Proficiency And Fail To Provide Real-Life Language Usage Experiences.
This Language Learning Platform Aims To Change This By Addressing The Growing Need For Multi-Language Proficiency In Workplaces And Academic Settings, Providing An Effective And Engaging Language Learning Experience.
Current Language Learning Methods And Apps Often Fail To Develop Speaking And Writing Proficiency, Focusing Instead On Memorization And Standardized Tests. This Language Trainer Addresses This Gap By Offering Insights Into The Science Of Speech Production.
By Combining Visual Cues Of Oral Shapes With Auditory Input, Learners Can Master Pronunciation, A Significant Challenge In Language Acquisition.
This Research Will Include Obtaining Near-Perfect Voice Files For Machine Learning Model Training, Signal Processing Of The Voice And Video Files, Development And Comparison Of Machine Learning Models, Data Visualization Development, Incorporation Into The Mobile Test Suite, And Preliminary Testing.
The Machine Learning Algorithms Will Use The Insights Extracted From Students' Voice Data To Provide Learners With Highly Targeted, Fine-Tuned Activities.
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.
- Subawards Are Not Planned For This Award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Gwynn Oak,
Maryland
21207-6042
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 7% from $274,660 to $294,660.
Word Of Mouth Technologies was awarded
Project Grant 2323040
worth $294,660
from National Science Foundation in October 2023 with work to be completed primarily in Gwynn Oak Maryland United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I:A language learning app based on sound and mouth movements
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is advancing new language learning by incorporating facial and lip recognition along with sound analysis. This visual aspect of creating sounds is vital for mastering pronunciation, one of the significant hurdles of learning a foreign language and even improving a native language. Current language learning methods often fall short in helping learners achieve speaking proficiency and fail to provide real-life language usage experiences. This language learning platform aims to change this by addressing the growing need for multi-language proficiency in workplaces and academic settings, providing an effective and engaging language learning experience._x000D_ _x000D_ Current language learning methods and apps often fail to develop speaking and writing proficiency, focusing instead on memorization and standardized tests. This language trainer addresses this gap by offering insights into the science of speech production. By combining visual cues of oral shapes with auditory input, learners can master pronunciation, a significant challenge in language acquisition. This research will include obtaining near-perfect voice files for machine learning model training, signal processing of the voice and video files, development and comparison of machine learning models, data visualization development, incorporation into the mobile test suite, and preliminary testing. The machine learning algorithms will use the insights extracted from students' voice data to provide learners with highly targeted, fine-tuned activities._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.
Topic Code
LC
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 8/13/24
Period of Performance
10/1/23
Start Date
9/30/24
End Date
Funding Split
$294.7K
Federal Obligation
$0.0
Non-Federal Obligation
$294.7K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2323040
Additional Detail
Award ID FAIN
2323040
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
FF13BNHWLH18
Awardee CAGE
8Z3B9
Performance District
MD-07
Senators
Benjamin Cardin
Chris Van Hollen
Chris Van Hollen
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $274,660 | 100% |
Modified: 8/13/24