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dc.contributor.advisorZhao, Yunxineng
dc.contributor.authorHu, Rong, 1972-eng
dc.date.issued2007eng
dc.date.submitted2007 Springeng
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 September 25, 2007)eng
dc.descriptionVita.eng
dc.descriptionThesis (Ph. D.) University of Missouri-Columbia 2007.eng
dc.description.abstractThe development of automatic speech recognition (ASR) technology has enabled an increasing number of applications. However, the robustness of ASR under real acoustic environments still remains to be a challenge for practical applications. Interfering speech and background noise have severe degrading effects on ASR. Speech source separation separates target speech from interfering speech but its performance is affected by adverse environmental conditions of acoustical reverberation and background noise. This dissertation works on the enhancement of a speech source separation technique, namely adaptive decorrelation filtering (ADF), for robust ASR applications. To overcome these difficulties and develop practical ADF speech separation algorithms for robust ASR, improvements are introduced in several aspects. From the perspectives of speech spectral characteristics, prewhitening procedures are applied to flatten the long-term speech spectrum to improve adaptation robustness and decrease ADF estimation error. To speedup convergence rate, block-iterative implementation and variable step-size (VSS) methods are proposed. To exploit scenarios where multiple pairs of sensors are available, multi-ADF postprocessing is developed. To overcome the limitations of ADF separation model under background noise, procedures of noise-compensation (NC) and adaptive speech enhancement are proposed for the achievement of improved robustness in diffuse noise. Speech separation simulations and speech recognition experiments are carried out based on TIMIT database and ATR acoustic measurement database. Evaluations of the methods presented in this dissertation demonstrate significant improvement of performances over baseline ADF algorithm in speech separation and recognition.eng
dc.description.bibrefIncludes bibliographical referenceseng
dc.identifier.merlinb59733305eng
dc.identifier.oclc173276044eng
dc.identifier.urihttps://doi.org/10.32469/10355/4682eng
dc.identifier.urihttps://hdl.handle.net/10355/4682
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.
dc.subject.lcshAutomatic speech recognitioneng
dc.subject.lcshSpeech perceptioneng
dc.subject.lcshSeparation (Technology)eng
dc.subject.lcshNoise controleng
dc.titleEnhancement of adaptive de-correlation filtering separation model for robust 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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