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dc.contributor.advisorÇetin, A. Enis
dc.contributor.authorJabloun, Firas
dc.date.accessioned2016-01-08T20:15:20Z
dc.date.available2016-01-08T20:15:20Z
dc.date.issued1998
dc.identifier.urihttp://hdl.handle.net/11693/18003
dc.descriptionAnkara : Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1998.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 1998.en_US
dc.descriptionIncludes bibliographical references leaves 48-52en_US
dc.description.abstractA ІКПѴ set of speech feature parameters based on multirate subband analysis and the Teager Energy Operator (TEO) is developed. The speech signal is first divided into nonuniform subbands in mel-scale using a multirate filter-bank, then the Teager energies of the subsignals are estimated. Finally, the feature vector is constructed by logcompression and inverse DOT computation. The new feature parameters (TEOCEP) have a robust speech recognition performance in car engine noise which has a low pass nature. In this thesis, we also present some solutions to the problem of large vocabulary speech recognition. Triphone-based Hidden Markov. Models (HMM) are used to model the vocabulary words. Although the straight forward parallel search strategy gives good recognition performance, the processing time required is found to be long and impractical. Therefore another search strategy with similar performance is described. Subvocabularies are developed during the training session to reduce the total number of words considered in the search process. The search is then performed in a tree structure by investigating one subvocabulary instead of all the words.en_US
dc.description.statementofresponsibilityJabloun, Firasen_US
dc.format.extent52 leavesen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSpeech recognitionen_US
dc.subjectMultirate subband anttlysisen_US
dc.subjectTeager Energy Operatoren_US
dc.subjectNonlinear speech modeling.en_US
dc.subjectTriphonesen_US
dc.subjectTree structure search strategyen_US
dc.subject.lccTK7895.S65 J33 1998en_US
dc.subject.lcshAutomatic speech recognition.en_US
dc.subject.lcshNatural language processing(Computer science.en_US
dc.titleLarge vocabulary speech recognition in noisy environmentsen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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