Gaussian mixture models
January 1, 2009
Journal Article
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Article in Encyclopedia of Biometrics, 2009, pp. 659-63. DOI: https://doi.org/10.1007/978-0-387-73003-5_196
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Summary
A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system, such as vocal-tract related spectral features in a speaker recognition system. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm or Maximum A Posteriori (MAP) estimation from a well-trained prior model.