Gaussian mixture models design and applications

Date

2000

Editor(s)

Advisor

Çetin, A. Enis

Supervisor

Co-Advisor

Co-Supervisor

Instructor

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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.

Source Title

Publisher

Course

Other identifiers

Book Title

Degree Discipline

Electrical and Electronic Engineering

Degree Level

Master's

Degree Name

MS (Master of Science)

Citation

Published Version (Please cite this version)

Language

English

Type