Automated Test of Word Recognition - Phase II

Award Information
Agency:
Department of Health and Human Services
Branch
n/a
Amount:
$743,628.00
Award Year:
2005
Program:
STTR
Phase:
Phase II
Contract:
2R42DC006509-02A1
Award Id:
66271
Agency Tracking Number:
DC006509
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
4410 Dellwood St., Arden Hills, MN, 55112
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
ROBERT MARGOLIS
(651) 639-1985
margo001@umn.edu
Business Contact:
ROBERT MARGOLIS
(651) 639-1985
MARGO001@UMN.EDU
Research Institute:
THE UNIVERSITY OF MINNESOTA

UNIVERSITY OF MINNESOTA
100 CHURCH ST SE
MINNEAPOLIS, MN, 55455

Nonprofit college or university
Abstract
DESCRIPTION (provided by applicant): Over 5 million word recognition tests are administered annually by audiologists in the United States with an associated cost of more than $100 million. These tests are currently performed manually by highly trained audiologists. This application describes the Phase II development of automated clinical speech recognition tests using clinical test recordings and an automated speech recognition system to score the subjects' responses. A method for automatically interpreting the test scores will also be evaluated. The objectives are to increase the accuracy and efficiency of these clinical tests, substantially reduce the cost, and provide an objective, automatic, evidence-based method for interpreting the results. The automated speech recognition test in combination with the automated pure tone audiogram (currently an STTR Phase II project) will perform diagnostic testing of a majority of audiology patients, freeing the audiologists' time for activities that require their training and skill. Contemporary changes in training and reimbursement patterns create a high demand for automated clinical procedures. The automated procedures are implemented on existing commercial audiometers with a personal computer that controls the audiometer delivery and routing of stimuli. Phase I results were obtained with automatic speech recognizers that were trained on a limited number of subjects (n=9). Estimates of the agreement between human and machine scoring ranged from 82-93%. Additional refinements with benefits that are predictable from prior experience will increase recognizer performance to a level that equals or exceeds human-human agreement and provide the basis for efficient and accurate clinical tests. In Phase II, an automatic speech recognition threshold test will be compared to the manual method used in routine clinical practice. Two different recognizer scoring strategies will be developed, one that requires more test time but is independent of individual speaker differences and is easily adaptable to other languages, and one that requires less time but may not be applicable to all patients. A pilot study will test the method on a Spanish-language speech-recognition test.

* information listed above is at the time of submission.

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