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Optimization of Gaussian Mixture Model Parameters for Speaker Identification
Q.Y. Hong, Sam Kwong, and H.L. Wang
Department of Computer Science, City University of Hong Kong, Hong Kong, Chinaqyhong@cs.cityu.edu.hk
cssamk@cityu.edu.hk
wanghl@cityu.edu.hk
Abstract. Gaussian mixture model (GMM) [1] has been widely used for modeling speakers. In speaker identification, one major problem is how to generate a set of GMMs for identification purposes based upon the training data. Due to the hill-climbing characteristic of the maximum likelihood (ML) method, any arbitrary estimate of the initial model parameters will usually lead to a sub-optimal model in practice. To resolve this problem, this paper proposes a hybrid training method based on the genetic algorithm (GA). It utilizes the global searching capability of the GA and combines the effectiveness of the ML method.
LNCS 3103, p. 1310 f.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004