標題: A model-selection-based self-splitting Gaussian mixture learning with application to speaker identification
作者: Cheng, SS
Wang, HM
Fu, HC
資訊工程學系
Department of Computer Science
關鍵字: unsupervised learning;Gaussian mixture modelling;Bayesian information criterion;speaker identification
公開日期: 15-十二月-2004
摘要: 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.
URI: http://dx.doi.org/10.1155/S1110865704407100
http://hdl.handle.net/11536/25551
ISSN: 1110-8657
DOI: 10.1155/S1110865704407100
期刊: EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING
Volume: 2004
Issue: 17
起始頁: 2626
結束頁: 2639
顯示於類別:期刊論文


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