EURASIP Journal on Applied Signal Processing
Volume 2004 (2004), Issue 17, Pages 2626-2639
doi:10.1155/S1110865704407100

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

Shih-Sian Cheng,1,2 Hsin-Min Wang,1 and Hsin-Chia Fu2

1Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
2Department of Computer Science and Information Engineering, National Chiao-Tung University, Hsinchu 300, Taiwan

Received 3 December 2003; Revised 2 July 2004

Academic Editor: Kenneth Barner

Copyright © 2004 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.