A self-growing probabilistic decision-based neural network for anchor/speaker identification

Authors:
Y. H. Chen;C. L. Tseng;Hsin-Chia Fu;H. T. Pao
Affiliations:
Department of Computer Science and Information Engineering, National Chiao-Tung University, Hsinchu, Taiwan ROC;Department of Computer Science and Information Engineering, National Chiao-Tung University, Hsinchu, Taiwan ROC;Department of Computer Science and Information Engineering, National Chiao-Tung University, Hsinchu, Taiwan ROC;Department of Management Science, National Chiao-Tung University, Hsinchu, Taiwan ROC
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

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Abstract

In this paper, we propose a new clustering algorithm for a mixture Gaussian based neural network, called Self-growing Probabilistic decision-based neural networks (SPDNN). The proposed Self-growing cluster learning (SGCL) algorithm is able to find the natural number of prototypes based on a self-growing validity measure, Bayesian Information Criterion (BIC). The learning process starts with a single prototype randomly initialized in the feature space and grows adaptively during the learning process until most appropriate number of prototypes are found. We have conduct numerical and real world experiments to demostrate the effectiveness of the SGCL algorithm. In the results of using SGCL to trainin the SPDNN for anchor/speaker identification, we have observed noticeable improvement among various model-based or vector quantization-based classification schemes.