dc.contributor.authorOoi, Mun Siang
dc.date.accessioned2015-06-09T03:36:46Z
dc.date.available2015-06-09T03:36:46Z
dc.date.copyright2005en_US
dc.date.issued2005
dc.identifier.urihttp://hdl.handle.net/10356/64891
dc.description.abstractFor speech recognition, Hidden Markov Model (HMM) is a popular approach as the classifier with high degree of accuracy; Adaptive Boosting (Adaboost) is a method to improve the performance of a given base classifier. In this study, Adaboost technique is applied to HMM classifier in speech recognition to test the resulting performance. Experiments on several speech corpora showed that Adaboost-HMM classifiers are significantly more accurate than the baseline HMM classifiers. Results also showed that sufficient training samples that cover most of the entire sample space is necessary for generalization of Adaboost-HMM classifiers.en_US
dc.format.extent87 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processingen_US
dc.titleSpeech recognition using Adaboost HMMen_US
dc.typeThesis
dc.contributor.supervisorFoo Say Weien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMSC(SIGNAL PROCESSING)en_US


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