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dc.contributor.advisorZhao, Yunxineng
dc.contributor.authorHu, Rusheng, 1971-eng
dc.date.issued2006eng
dc.date.submitted2006 Falleng
dc.descriptionThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.eng
dc.descriptionTitle from title screen of research.pdf file (viewed on August 2, 2007)eng
dc.descriptionVita.eng
dc.descriptionThesis (Ph. D.) University of Missouri-Columbia 2006.eng
dc.description.abstractThis dissertation investigates optimization of acoustic models in speech recognition. Two new optimization methods are proposed for phonetic decision tree (PDT) search and Hidden Markov modeling (HMM)-- the knowledge-based adaptive PDT algorithm and the HMM gradient boosting algorithm. Investigations are conducted to applying both methods to improve word error rate of the state-of-the-art speech recognition system. However, these two methods are developed in a general machine learning background and their applications are not limited to speech recognition. The HMM gradient boosting method is based on a function approximation scheme from the perspective of optimization in function space rather than the parameter space, based on the fact that the Gaussian mixture model in each HMM state is an additive model of homogeneous functions (Gaussians). It provides a new scheme which can jointly optimize model structure and parameters. Experiments are conducted on the World Street Journal (WSJ) task and good improvements on word error rate are observed. The knowledge-based adaptive PDT algorithm is developed under a trend toward knowledge-based systems and aims at optimizing the mapping from contextual phones to articulatory states by maximizing implicit usage of the phonological and phonetic information, which is presumed to be contained in large data corpus. A computational efficient algorithm is developed to incorporate this prior knowledge in PDT construction. This algorithm is evaluated on the Telehealth conversational speech recognition and significant improvement on system performance is achieved.eng
dc.description.bibrefIncludes bibliographical referenceseng
dc.identifier.merlinb59269194eng
dc.identifier.oclc162129635eng
dc.identifier.urihttps://doi.org/10.32469/10355/4329eng
dc.identifier.urihttps://hdl.handle.net/10355/4329
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. Copyright held by author.
dc.subjectphonetic decision tree.eng
dc.subjectphonetic decision treeeng
dc.subject.lcshSpeech perceptioneng
dc.subject.lcshPattern recognition systemseng
dc.subject.lcshHidden Markov modelseng
dc.titleStatistical optimization of acoustic models for large vocabulary speech recognitioneng
dc.typeThesiseng
thesis.degree.disciplineComputer science (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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