dc.contributor.authorLi, Guojieen_US
dc.date.accessioned2008-09-17T09:55:32Z
dc.date.accessioned2017-07-23T08:31:54Z
dc.date.available2008-09-17T09:55:32Z
dc.date.available2017-07-23T08:31:54Z
dc.date.copyright2005en_US
dc.date.issued2005
dc.identifier.citationLi, G. (2005). Radial basis function neutral networks for speaker verification. Master’s thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/4626
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_US
dc.rightsNanyang Technological Universityen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies
dc.titleRadial basis function neutral networks for speaker verificationen_US
dc.typeThesisen_US
dc.contributor.supervisor2Sundararajan Narasimhanen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.supervisorSaratchandran Paramasivanen_US
dc.description.degreeMASTER OF ENGINEERING (EEE)en_US


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