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Neural Computation

June 2009, Vol. 21, No. 6, Pages 1776-1795
(doi: 10.1162/neco.2008.04-08-776)
© 2008 Massachusetts Institute of Technology
Parameter Estimation for α-GMM Based on Maximum Likelihood Criterion
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α-integration and α-GMM have been recently proposed for integrated stochastic modeling. However, there has not been an approach to date for estimating model parameters for α-GMM in a statistical way, based on a set of training data. In this letter, parameter updating formulas are mathematically derived based on maximum likelihood criterion using an adapted expectation-maximization algorithm. With this method, model parameters for α-GMM are reestimated in an iterative way. The updating formulas were found to be simple and systematically compatible with the GMM equations. This advantage renders the α-GMM a superset of the GMM but with similar computational complexity. This method has been effectively applied to realistic speaker recognition applications.