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

Autor: Shih-Sian Cheng, Hsin-Chia Fu, Hsin-Min Wang
Jazyk: angličtina
Rok vydání: 2004
Předmět:
Zdroj: EURASIP Journal on Advances in Signal Processing, Vol 2004, Iss 17, Pp 2626-2639 (2004)
Druh dokumentu: article
ISSN: 16876172
1687-6172
1687-6180
DOI: 10.1155/S1110865704407100
Popis: 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.
Databáze: Directory of Open Access Journals
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