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SSRN Electronic Journal, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Firpo, Sergio +4 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Firpo, Sergio +4 more
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Implied Probabilities in GMM Estimators
Econometrica, 1993The conventional way to estimate a distribution function is to assume it belongs to a class parameterized by a finite-dimensional vector and then estimate the unknown parameter vector. In many cases, e.g., regression models, part of the assumption is of the form: a given function of the data and of the parameter vector has a zero mean.
Back, Kerry, Brown, David P
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Comparison of GMM and fuzzy-GMM applied to phoneme classification
2009 3rd International Conference on Signals, Circuits and Systems (SCS), 2009The increasing need for more natural human machine interfaces has generated intensive research work directed toward designing and implementing natural speech enabled systems. Because it is very hard to constrain a speaker when expressing a voice-based request, speech recognition systems have to be able to handle out of vocabulary words in the users ...
Kacem Abida, Fakhri Karray, Jiping Sun
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Econometric Theory, 2009
This paper proposes the least absolute shrinkage and selection operator–type (Lasso-type) generalized method of moments (GMM) estimator. This Lasso-type estimator is formed by the GMM objective function with the addition of a penalty term. The exponent of the penalty term in the regular Lasso estimator is equal to one.
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This paper proposes the least absolute shrinkage and selection operator–type (Lasso-type) generalized method of moments (GMM) estimator. This Lasso-type estimator is formed by the GMM objective function with the addition of a penalty term. The exponent of the penalty term in the regular Lasso estimator is equal to one.
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2004 International Conference on Signal Processing and Communications, 2004. SPCOM '04., 2005
In this paper, we propose the shared mixture GMM classifier. Mixtures of a GMM which represent areas in the feature space must have very less overlap across classes for best performance. If not, patterns of a class belonging to regions of overlap score high likelihoods with not only the mixture of its own class, but also that mixture of another class ...
A.G. Krishna, T.V. Sreenivas
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In this paper, we propose the shared mixture GMM classifier. Mixtures of a GMM which represent areas in the feature space must have very less overlap across classes for best performance. If not, patterns of a class belonging to regions of overlap score high likelihoods with not only the mixture of its own class, but also that mixture of another class ...
A.G. Krishna, T.V. Sreenivas
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IEEE Robotics and Automation Letters, 2018
This letter presents a robust Gaussian mixture model registration technique to enable mapping and navigation in dark, complex, unstructured domains such as caves and mines. Subterranean environments are often unmapped and challenged by low-lighting conditions and communication constraints.
Wennie Tabib +2 more
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This letter presents a robust Gaussian mixture model registration technique to enable mapping and navigation in dark, complex, unstructured domains such as caves and mines. Subterranean environments are often unmapped and challenged by low-lighting conditions and communication constraints.
Wennie Tabib +2 more
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GMM-based significance decoding
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013The accuracy of automatic speech recognition systems in noisy and reverberant environments can be improved notably by exploiting the uncertainty of the estimated speech features using so-called uncertainty-of-observation techniques. In this paper, we introduce a new Bayesian decision rule that can serve as a mathematical framework from which both known
Ahmed Hussen Abdelaziz +4 more
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Enhanced Speaker Verification Using GMM-Supervector Based Modified Adaptive GMM Training
2015In this paper, an enhanced speaker verification is proposed by exploring a novel modified adaptive Gaussian mixture model (GMM) training. Based weight factor of observation called the observation reliability; we propose to apply a modified Expectation maximization (EM) algorithm, combined with a modified Maximum a posteriori (MAP) estimation to train ...
Tan Dat Trinh +4 more
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