Sample iterative likelihood maximization for speaker verification systems
Paper in proceedings, 2010

Gaussian Mixture Models (GMMs) have been the dominant technique used for modeling in speaker recognition systems. Traditionally, the GMMs are trained using the Expectation Maximization (EM) algorithm and a large set of training samples. However, the convergence of the EM algorithm to a global maximum is conditioned on proper parameter initialization, a large enough training sample set, and several iterations over this training set. In this work, a Sample Iterative Likelihood Maximization (SILM) algorithm based on a stochastic descent gradient method is proposed. Simulation results showed that our algorithm can attain high loglikelihoods with fewer iterations in comparison to the EMalgorithm. A maximum of eight times faster convergence rate can be achieved in comparison with the EM algorithm.

Author

Guillermo Garcia

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

Thomas Eriksson

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

18th European Signal Processing Conference, EUSIPCO 2010; Aalborg; Denmark; 23 August 2010 through 27 August 2010

2219-5491 (ISSN)

596-600

Subject Categories

Communication Systems

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Created

10/7/2017