Gaussian mixture models design and applications
Author
Ben Fatma, Khaled
Advisor
Çetin, A. Enis
Date
2000Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
Two new design algorithms for estimating the parameters of Gaussian Mixture
Models (GMh-l) are developed. These algorithms are based on fitting a
GMM on the histogram of the data. The first method uses Least Squares Error
(LSE) estimation with Gaus,s-Newton optimization technique to provide more
accurate GMM parameter estimates than the commonl}' used ExpectationMaximization
(EM) algorithm based estimates. The second method employs
the matching pursuit algorithm which is based on finding the Gaussian functions
that best match the individual components of a GMM from an overcomplete
set. This algorithm provides a fast method for obtaining GMM parameter
estimates.
The proposed methods can be used to model the distribution of a large set of
arbitrary random variables. Application of GMMs in human skin color density
modeling and speaker recognition is considered. For speaker recognition, a
new set of speech fiiature jmrameters is developed. The suggested set is more
appropriate for speaker recognition applications than the widely used Mel-scale
based one.
Keywords
Gaussian Mixture ModelsParameter Estimation
Expectation-Maximization Algorithm
Gauss-Newton Algorithm
Matching Pursuit Algorithm
Least Sciuares Error
Speaker Recognition