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Speaker verification using Adapted Gaussian mixture models
 Digital Signal Processing
, 2000
"... In this paper we describe the major elements of MIT Lincoln Laboratory’s Gaussian mixture model (GMM)based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but ef ..."
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Cited by 976 (42 self)
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In this paper we describe the major elements of MIT Lincoln Laboratory’s Gaussian mixture model (GMM)based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple
The Infinite Gaussian Mixture Model
 In Advances in Neural Information Processing Systems 12
, 2000
"... In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the "right" number of mixture components. Inference in the model is ..."
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Cited by 256 (8 self)
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In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the "right" number of mixture components. Inference in the model
Discriminant Analysis by Gaussian Mixtures
 Journal of the Royal Statistical Society, Series B
, 1996
"... FisherRao linear discriminant analysis (LDA) is a valuable tool for multigroup classification. LDA is equivalent to maximum likelihood classification assuming Gaussian distributions for each class. In this paper, we fit Gaussian mixtures to each class to facilitate effective classification in nonn ..."
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Cited by 207 (9 self)
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FisherRao linear discriminant analysis (LDA) is a valuable tool for multigroup classification. LDA is equivalent to maximum likelihood classification assuming Gaussian distributions for each class. In this paper, we fit Gaussian mixtures to each class to facilitate effective classification in non
Progressive Gaussian Mixture Reduction
 in Proceedings of the 11th International Conference on Information Fusion (Fusion 2008
, 2008
"... Abstract—For estimation and fusion tasks it is inevitable to approximate a Gaussian mixture by one with fewer components to keep the complexity bounded. Appropriate approximations can be typically generated by exploiting the redundancy in the shape description of the original mixture. In contrast to ..."
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Cited by 8 (5 self)
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Abstract—For estimation and fusion tasks it is inevitable to approximate a Gaussian mixture by one with fewer components to keep the complexity bounded. Appropriate approximations can be typically generated by exploiting the redundancy in the shape description of the original mixture. In contrast
A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models
, 1997
"... We describe the maximumlikelihood parameter estimation problem and how the Expectationform of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) fi ..."
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Cited by 678 (4 self)
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We describe the maximumlikelihood parameter estimation problem and how the Expectationform of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2
Gaussian Mixture Models
"... We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixture Model structure, and the means and mixture weights vary in a subspace of the total parameter space. We call this a Subspace Gaussian Mixture Model (SGMM). Globally shared parameters define the subs ..."
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We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixture Model structure, and the means and mixture weights vary in a subspace of the total parameter space. We call this a Subspace Gaussian Mixture Model (SGMM). Globally shared parameters define
Truncated Gaussian Mixture Model
"... This article address a novel emotion recognition system based on the Truncated Gaussian mixture model.The proposed system has been experimented over an gender independent emotion recognition database In the recent past, many models have been listed in the literature based on the emotion recognition, ..."
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This article address a novel emotion recognition system based on the Truncated Gaussian mixture model.The proposed system has been experimented over an gender independent emotion recognition database In the recent past, many models have been listed in the literature based on the emotion recognition
HIERARCHICAL GAUSSIAN MIXTURE MODEL
"... Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image processing to machine learning, this statistical mixture modeling is usually complex and further need to be simplified. I ..."
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Cited by 9 (2 self)
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Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image processing to machine learning, this statistical mixture modeling is usually complex and further need to be simplified
Continuous Gaussian mixture modeling, in
 Gindi (Eds.), Information Processing in Medical Imaging. Springer Lecture Notes in Computer Science 1230
, 1997
"... Abstract. When the projection of a collection of samples onto a subset of basis feature vectors has a Gaussian distribution, those samples have a generalized projective Gaussian distribution (GPGD). GPGDs arise in a variety of medical images as well as some speech recognition problems. We will demon ..."
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Cited by 1 (1 self)
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will demonstrate that GPGDs are better represented by continuous Gaussian mixture models (CGMMs) than fmite Gaussian mixture models (FGMMs). This paper introduces a novel technique for the automated specification of CGMMs, height ridges of goodnessoffit. For GPGDs, Monte Carlo simulations and ROC analysis
Results 1  10
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137,621