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A Model-Selection-Based Self-Splitting Gaussian Mixture Learning with Application to Speaker Identification

EURASIP Journal on Advances in Signal Processing20042004:312192

DOI: 10.1155/S1110865704407100

Received: 3 December 2003

Published: 27 December 2004

Abstract

We propose a self-splitting Gaussian mixture learning (SGML) algorithm for Gaussian mixture modelling. The SGML algorithm is deterministic and is able to find an appropriate number of components of the Gaussian mixture model (GMM) based on a self-splitting validity measure, Bayesian information criterion (BIC). It starts with a single component in the feature space and splits adaptively during the learning process until the most appropriate number of components is found. The SGML algorithm also performs well in learning the GMM with a given component number. In our experiments on clustering of a synthetic data set and the text-independent speaker identification task, we have observed the ability of the SGML for model-based clustering and automatically determining the model complexity of the speaker GMMs for speaker identification.

Keywords

unsupervised learning Gaussian mixture modelling Bayesian information criterion speaker identification

Authors’ Affiliations

(1)
Institute of Information Science, Academia Sinica
(2)
Department of Computer Science and Information Engineering, National Chiao-Tung University

Copyright

© Cheng et al. 2004

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