Synonyms
Gaussian mixture density; GMM
Definition
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.
Introduction
A Gaussian mixture model is a weighted sum of M component Gaussian densities as given by the equation,
where x is a D-dimensional continuous-valued data vector (i.e. measurement or features), w i , i = 1, …, M, are the mixture weights, and \(g({\bf x}\vert...
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References
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Reynolds, D. (2009). Gaussian Mixture Models. In: Li, S.Z., Jain, A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73003-5_196
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DOI: https://doi.org/10.1007/978-0-387-73003-5_196
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