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dc.contributor.authorBaghdasaryan, Areg Gagiken_US
dc.date.accessioned2014-03-14T20:31:38Z
dc.date.available2014-03-14T20:31:38Z
dc.date.issued2010-01-27en_US
dc.identifier.otheretd-02082010-174617en_US
dc.identifier.urihttp://hdl.handle.net/10919/31182
dc.description.abstractA speaker independent continuous speech phoneme recognition and segmentation system is presented. We discuss the training and recognition phases of the phoneme recognition system as well as a detailed description of the integrated elements. The Hidden Markov Model (HMM) based phoneme models are trained using the Baum-Welch re-estimation procedure. Recognition and segmentation of the phonemes in the continuous speech is performed by a Segmental Viterbi Search on a Segmental Ergodic HMM for the phoneme states. We describe in detail the three phases of the phoneme joint recognition and segmentation system. First, the extraction of the Mel-Frequency Cepstral Coefficients (MFCC) and the corresponding Delta and Delta Log Power coefficients is described. Second, we describe the operation of the Baum-Welch re-estimation procedure for the training of the phoneme HMM models, including the K-Means and the Expectation-Maximization (EM) clustering algorithms used for the initialization of the Baum-Welch algorithm. Additionally, we describe the structural framework of - and the recognition procedure for - the ergodic Segmental HMM for the phoneme segmentation and recognition. We include test and simulation results for each of the individual systems integrated into the phoneme recognition system and finally for the phoneme recognition/segmentation system as a whole.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartBaghdasaryan_AG_T_2010.pdfen_US
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectViterbien_US
dc.subjectBaum Welchen_US
dc.subjectHidden Markov Modelen_US
dc.subjectSegmental HMMen_US
dc.subjectClusteren_US
dc.subjectSpeechen_US
dc.subjectSpeakeren_US
dc.titleAutomatic Phoneme Recognition with Segmental Hidden Markov Modelsen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.committeechairBeex, A. A. Louisen_US
dc.contributor.committeememberWyatt, Christopher L.en_US
dc.contributor.committeememberda Silva, Claudio R. C. M.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-02082010-174617/en_US
dc.date.sdate2010-02-08en_US
dc.date.rdate2010-03-10
dc.date.adate2010-03-10en_US


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