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A symmetrization of the Subspace Gaussian Mixture Model
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011Last year we introduced the Subspace Gaussian Mixture Model (SGMM), and we demonstrated Word Error Rate improvements on a fairly small-scale task. Here we describe an extension to the SGMM, which we call the symmetric SGMM. It makes the model fully symmetric between the “speech-state vectors” and “speaker vectors” by making the mixture weights depend ...
Daniel Povey +3 more
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2014
In this chapter we first introduce the basic concepts of random variables and the associated distributions. These concepts are then applied to Gaussian random variables and mixture-of-Gaussian random variables. Both scalar and vector-valued cases are discussed and the probability density functions for these random variables are given with their ...
Dong Yu, Li Deng
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In this chapter we first introduce the basic concepts of random variables and the associated distributions. These concepts are then applied to Gaussian random variables and mixture-of-Gaussian random variables. Both scalar and vector-valued cases are discussed and the probability density functions for these random variables are given with their ...
Dong Yu, Li Deng
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On the number of components in a Gaussian mixture model
WIREs Data Mining and Knowledge Discovery, 2014Mixture distributions, in particular normal mixtures, are applied to data with two main purposes in mind. One is to provide an appealing semiparametric framework in which to model unknown distributional shapes, as an alternative to, say, the kernel density method.
Geoffrey J. McLachlan +1 more
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The Infinite Gaussian Mixture Model.
2000In 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.
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Unsupervised Parameterisation of Gaussian Mixture Models
2002In this paper we explain a new practical methodology to fully parameterise Gaussian Mixture Models (GMM) to describe data set distributions. Our approach analyses hierarchically a data set distribution to be modeled, determining unsupervisedly an appropriate number of components of the GMM, and their corresponding parameterisation. Results are provided
Daniel Ponsa, F. Xavier Roca
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Multivariate Scale Mixture of Gaussians Modeling
2006In this paper, we present an approach to generate a class of multivariate probability models, which are referred to as scale mixture of Gaussians models. They are constructed as normal variance mixture models, in which the covariance matrix involves a stochastic scale factor with a given prior distribution.
Torbjørn Eltoft, Taesu Kim, Te-Won Lee
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Earthquake Phase Association Using a Bayesian Gaussian Mixture Model
Journal of Geophysical Research: Solid Earth, 2022Weiqiang Zhu +2 more
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A Network Traffic Anomaly Detection Method Based on Gaussian Mixture Model
Electronics (Switzerland), 2023Bin Yu, Yuliang Wei
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Statistical Representation of Distribution System Loads Using Gaussian Mixture Model
IEEE Transactions on Power Systems, 2010Ravindra Singh +2 more
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