Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/4626
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dc.contributor.authorLi, Guojieen
dc.date.accessioned2008-09-17T09:55:32Zen
dc.date.available2008-09-17T09:55:32Zen
dc.date.copyright2005en
dc.date.issued2005en
dc.identifier.citationLi, G. (2005). Radial basis function neutral networks for speaker verification. Master’s thesis, Nanyang Technological University, Singapore.en
dc.identifier.urihttps://hdl.handle.net/10356/4626en
dc.description.abstractThis thesis presents the application of a minimal radial basis function (RBF) neural network, referred to as MRAN (Minimal Resource Allocation Network) for speaker verification. Extension of MRAN to elliptical basis functions has been studied too. MRAN is a sequential learning algorithm for radial basis function neural networks. During the training, MRAN allows hidden neurons to be added or removed thus to realize a minimal network. MRAN recruits hidden neurons based on the novelty of the input data. If all of the novelty criteria can not be satisfied, the existing network parameters are updated by extended Kalman filter (EKF). Additionally, MRAN’s pruning strategy removes hidden neurons from the network if their contributed output to the output layer is insignificant. In this way, MRAN is adapted to fit the dynamics of the input data closely.en
dc.rightsNanyang Technological Universityen
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systemsen
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologiesen
dc.titleRadial basis function neutral networks for speaker verificationen
dc.typeThesisen
dc.contributor.supervisorSaratchandran Paramasivanen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.description.degreeMASTER OF ENGINEERING (EEE)en
dc.contributor.supervisor2Sundararajan Narasimhanen
dc.identifier.doi10.32657/10356/4626en
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