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...
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References
J. Benesty, M. Sondhi, Y. Huang (eds.), Springer Handbook of Speech Processing, vol. XXXVI (Springer, Berlin, 2008)
C. Müler (ed.), Speaker Classification I: Fundamentals, Features, and Methods. Lecture Notes in Computer Science, vol. 4343/2007 (Springer, Berlin, 2007)
R. Gray, Vector quantization. IEEE ASSP Mag. 1(2), 4–29 (1984)
D.A. Reynolds, A Gaussian mixture modeling approach to text-independent speaker identification. PhD thesis, Georgia Institute of Technology, 1992
D.A. Reynolds, R.C. Rose, Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Trans. Acoust. Speech Signal Process. 3(1), 72–83 (1995)
G. McLachlan (ed.), Mixture Models (Marcel Dekker, New York, 1988)
A. Dempster, N. Laird, D. Rubin, Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39(1), 1–38 (1977)
D.A. Reynolds, T.F. Quatieri, R.B. Dunn, Speaker verification using adapted Gaussian mixture models. Digit. Signal Process. 10(1), 19–41 (2000)
Acknowledgements
This work was sponsored by the Department of Defense under Air Force Contract FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the US Government.
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Reynolds, D. (2015). Gaussian Mixture Models. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_196
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