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dc.contributor.advisorClements, Mark A.
dc.contributor.authorAli, Asif
dc.date.accessioned2014-01-13T16:51:03Z
dc.date.available2014-01-13T16:51:03Z
dc.date.created2013-12
dc.date.issued2013-11-18
dc.date.submittedDecember 2013
dc.identifier.urihttp://hdl.handle.net/1853/50363
dc.description.abstractAn ergodic hidden Markov model (EHMM) can be useful in extracting underlying structure embedded in connected speech without the need for a time-aligned transcribed corpus. In this research, we present a query-by-example (QbE) spoken term detection system based on an ergodic hidden Markov model of speech. An EHMM-based representation of speech is not invariant to speaker-dependent variations due to the unsupervised nature of the training. Consequently, a single phoneme may be mapped to a number of EHMM states. The effects of speaker-dependent and context-induced variation in speech on its EHMM-based representation have been studied and used to devise schemes to minimize these variations. Speaker-invariance can be introduced into the system by identifying states with similar perceptual characteristics. In this research, two unsupervised clustering schemes have been proposed to identify perceptually similar states in an EHMM. A search framework, consisting of a graphical keyword modeling scheme and a modified Viterbi algorithm, has also been implemented. An EHMM-based QbE system has been compared to the state-of-the-art and has been demonstrated to have higher precisions than those based on static clustering schemes.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectSpeech recognition
dc.subjectHidden Markov model
dc.subject.lcshErgodic theory
dc.subject.lcshHidden Markov models
dc.subject.lcshAutomatic speech recognition
dc.titleVoice query-by-example for resource-limited languages using an ergodic hidden Markov model of speech
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentElectrical and Computer Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberLee, Chin-Hui
dc.contributor.committeeMemberAnderson, David V.
dc.contributor.committeeMemberCopeland, John
dc.contributor.committeeMemberLerch, Alexander
dc.date.updated2014-01-13T16:51:04Z


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