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AUTOMATED REGRESSION TESTING APPROACH TO EXPANSION AND REFINEMENT OF SPEECH RECOGNITION GRAMMARS
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TitleAUTOMATED REGRESSION TESTING APPROACH TO EXPANSION AND REFINEMENT OF SPEECH RECOGNITION GRAMMARS
AuthorDookhoo, Raul
Keywordsregression testing
speech recognition
audio regression tester
ART
AbstractThis thesis describes an approach to automated regression testing for speech recognition grammars. A prototype Audio Regression Tester called ART has been developed using Microsoft's Speech API and C#. ART allows a user to perform any of three tasks: automatically generate a new XML-based grammar file from standardized SQL database entries, record and cross-reference audio files for use by an underlying speech recognition engine, and perform regression tests with the aid of an oracle grammar. ART takes as input a wave sound file containing speech and a newly created XML grammar file. It then simultaneously executes two tests: one with the wave file and the new grammar file and the other with the wave file and the oracle grammar. The comparison result of the tests is used to determine whether the test was successful or not. This allows rapid exhaustive evaluations of additions to grammar files to guarantee forward process as the complexity of the voice domain grows. The data used in this research to derive results were taken from the LifeLike project. However, the capabilities of ART extend beyond LifeLike. The results gathered have shown that using a person's recorded voice to do regression testing is as effective as having the person do live testing. A cost-benefit analysis, using two published equations, one for Cost and the other for Benefit, was also performed to determine if automated regression testing is really more effective than manual testing. Cost captures the salaries of the engineers who perform regression testing tasks and Benefit captures revenue gains or losses related to changes in product release time. ART had a higher benefit of $21461.08 when compared to manual regression testing which had a benefit of $21393.99. Coupled with its excellent error detection rates, ART has proven to be very efficient and cost-effective in speech grammar creation and refinement.
AdviserDeMara, Ronald
PublisherUniversity of Central Florida
DegreeM.S.
Degree DisciplineSchool of Electrical Engineering and Computer Science
Degree GrantorEngineering and Computer Science
Degree ProgramComputer Science MS
Graduation Date2008-01-01
TypeMaster's thesis
Access LevelPublic - Allow Worldwide Access
Release Date2008-12-20
RepositoryUniversity Archives
Repository CollectionElectronic Theses and Dissertations
IdentifierCFE0002437
Access Linkhttp://purl.fcla.edu/fcla/etd/CFE0002437

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