Open Access Research Article

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

Shih-Sian Cheng12*, Hsin-Min Wang1 and Hsin-Chia Fu2

Author Affiliations

1 Institute of Information Science, Academia Sinica, Taipei 115, Taiwan

2 Department of Computer Science and Information Engineering, National Chiao-Tung University, Hsinchu 300, Taiwan

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EURASIP Journal on Advances in Signal Processing 2004, 2004:312192  doi:10.1155/S1110865704407100

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