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35,987
Maximum likelihood from incomplete data via the EM algorithm
- JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
Abstract
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Cited by 11972 (17 self)
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situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.
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 ..."
Abstract
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Cited by 253 (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
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 ..."
Abstract
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Cited by 1010 (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
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 ..."
Abstract
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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 ..."
Abstract
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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 goodness-of-fit. For GPGDs, Monte Carlo simulations and ROC analysis
Gaussian Mixture Model
"... The speaker recognition task falls under the general problem of pattern classification. Speaker recognition as a pattern classification problem, its ultimate objective is design of a system that classifies the vector of features in different classes by partitioning the feature space into optimal spe ..."
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classes are far apart from each other. In this paper we discuss the issue of the application of LDA to our Gaussian Mixture Model (GMM) based speaker identification task. Applying LDA improved the identification performance.
GMM Gaussian Mixture Model
"... In recent years, rapid growth in wind energy as a substantial source of electricity generation has created greater demands on wind turbine system reliability and availability. To reduce service costs and maximize return on investment, wind farm operators have begun to take a more proactive approach ..."
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to turbine problems by relying on intelligent condition monitoring and automated failure detection systems. The challenge is how to effectively convert large amounts of data into actionable decisions to detect and isolate failures at an early stage. This paper describes a unique data analysis and modeling
Results 1 - 10
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35,987
