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dc.contributor.advisorNeagu, Daniel
dc.contributor.advisorHolton, Robert
dc.contributor.authorKhan, Wasiq
dc.date.accessioned2018-02-02T15:54:16Z
dc.date.available2018-02-02T15:54:16Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10454/14802
dc.description.abstractDynamic speech properties such as time warping, silence removal and background noise interference are the most challenging issues in continuous speech signal matching. Among all of them, the time warped speech signal matching is of great interest and has been a tough challenge for the researchers. An adaptive framing based continuous speech tracking and similarity measurement approach is introduced in this work following a comprehensive research conducted in the diverse areas of speech processing. A dynamic state model is introduced based on system of linear motion equations which models the input (test) speech signal frame as a unidirectional moving object along the template speech signal. The most similar corresponding frame position in the template speech is estimated which is fused with a feature based similarity observation and the noise variances using a Kalman filter. The Kalman filter provides the final estimated frame position in the template speech at current time which is further used for prediction of a new frame size for the next step. In addition, a keyword spotting approach is proposed by introducing wavelet decomposition based dynamic noise filter and combination of beliefs. The Dempster’s theory of belief combination is deployed for the first time in relation to keyword spotting task. Performances for both; speech tracking and keyword spotting approaches are evaluated using the statistical metrics and gold standards for the binary classification. Experimental results proved the superiority of the proposed approaches over the existing methods.en_US
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectSpeech tracking; Dynamic time warping; Kalman filter; Dynamic noise filtration; Adaptive framing; Keyword spotting; Template matching; Similarity measurement.en_US
dc.titleA Novel Approach for Continuous Speech Tracking and Dynamic Time Warping. Adaptive Framing Based Continuous Speech Similarity Measure and Dynamic Time Warping using Kalman Filter and Dynamic State Modelen_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentSchool of Electrical Engineering & Computer Scienceen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2014
dc.description.publicnotesThe appendices files are not available online.en
refterms.dateFOA2018-07-28T02:56:05Z


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