Radial basis function neutral networks for speaker verification

Radial basis function neutral networks for speaker verification

This 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 b...

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Bibliographic Details
Main Author: Li, Guojie
Other Authors: Saratchandran Paramasivan
Format: Theses and Dissertations
Published: 2008
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
DRNTU::Engineering::Computer science and engineering::Computing methodologies
Online Access:https://hdl.handle.net/10356/4626
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Institution: Nanyang Technological University
Description
Summary:This 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.

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