A Model-Selection-Based Self-Splitting Gaussian Mixture Learning with Application to Speaker Identification
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
EURASIP Journal on Advances in Signal Processing 2004, 2004:312192 doi:10.1155/S1110865704407100Published: 27 December 2004
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.