The Experts below are selected from a list of 58986 Experts worldwide ranked by ideXlab platform
Lucy Y. Pao - One of the best experts on this subject based on the ideXlab platform.
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Variance estimation and ranking of target tracking position errors modeled using Gaussian Mixture distributions
Automatica, 2005Co-Authors: L. Trailovic, Lucy Y. PaoAbstract:In this paper, variance estimation and ranking methods are developed for stochastic processes modeled by Gaussian Mixture distributions. It is shown that the variance estimate from a Gaussian Mixture distribution has the same properties as the variance estimate from a single Gaussian distribution based on a reduced number of samples. Hence, well-known tools for variance estimation and ranking of single Gaussian distributions can be applied to Gaussian Mixture distributions. As an application example, we present optimization of sensor processing order in the sequential multi-target multi-sensor joint probabilistic data association (MSJPDA) algorithm.
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Variance estimation and ranking of Gaussian Mixture distributions in target tracking applications
Proceedings of the 41st IEEE Conference on Decision and Control 2002., 1Co-Authors: L. Trailovic, Lucy Y. PaoAbstract:Variance estimation and ranking methods are developed for stochastic processes modeled by Gaussian Mixture distributions. It is shown that the variance estimate from a Gaussian Mixture distribution has the same properties as a variance estimate from a single Gaussian distribution based on a reduced number of samples. Hence, well known tools of variance estimation and ranking of single Gaussian distributions can be applied to Gaussian Mixture distributions. As an application example, optimization of sensor processing order in the sequential multi-target multi-sensor joint probabilistic data association algorithm is presented.
D.g. Bhalke - One of the best experts on this subject based on the ideXlab platform.
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Speaker verification using Gaussian Mixture Model
2015 International Conference on Pervasive Computing (ICPC), 2015Co-Authors: Shilpa S. Jagtap, D.g. BhalkeAbstract:In this paper, speaker verification system using Gaussian Mixture Model (GMM) is proposed. The proposed system consists of pre-processing, feature extraction, modelling and classification stage. The pre-processing is used to remove silent part of signal to reduce computational complexity. Pitch frequency and Mel Frequency Cepstral Coefficients(MFCC)are used as a feature vector for speaker verification system. Modelling is done using different combination of Gaussian Mixture models. Simple distance measures are used for the classification between reference and the test signal.
André Monin - One of the best experts on this subject based on the ideXlab platform.
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Modal Trajectory Estimation using Maximum Gaussian Mixture
IEEE Transactions on Automatic Control, 2013Co-Authors: André MoninAbstract:This paper deals with the estimation of the whole trajectory of a stochastic dynamic system with highest probability , conditionally upon the past observation process, using a maximum Gaussian Mixture. We first recall the Gaussian sum technique applied to minimum variance filtering. It is then shown that the same concept of Gaussian Mixture can be applied in that context, provided we replace the Sum operator by the Max operator.
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Modal Trajectory Estimation Using Maximum Gaussian Mixture
IEEE Transactions on Automatic Control, 2013Co-Authors: André MoninAbstract:This technical note deals with the estimation of the whole trajectory of a stochastic dynamic system with highest probability, conditionally upon the past observation process, using a maximum Gaussian Mixture. We first recall the Gaussian sum technique applied to minimum variance filtering. It is then shown that the same concept of Gaussian Mixture can be applied in that context, provided we replace the Sum operator by the Max operator.
Douglas A Reynolds - One of the best experts on this subject based on the ideXlab platform.
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Gaussian Mixture models
Encyclopedia of Biometrics, 2009Co-Authors: Douglas A ReynoldsAbstract:Definition A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system, such as vocal-tract related spectral features in a speaker recognition system. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm or Maximum A Posteriori (MAP) estimation from a well-trained prior model.
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speaker verification using adapted Gaussian Mixture models
Digital Signal Processing, 2000Co-Authors: Douglas A Reynolds, Thomas F Quatieri, Robert B DunnAbstract:Reynolds, Douglas A., Quatieri, Thomas F., and Dunn, Robert B., Speaker Verification Using Adapted Gaussian Mixture Models, Digital Signal Processing10(2000), 19Â?41.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 effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM. The development and use of a handset detector and score normalization to greatly improve verification performance is also described and discussed. Finally, representative performance benchmarks and system behavior experiments on NIST SRE corpora are presented.
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Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models
IEEE Transactions on Speech and Audio Processing, 1995Co-Authors: Douglas A Reynolds, Richard C. RoseAbstract:This paper introduces and motivates the use of Gaussian Mixture models (GMM) for robust text-independent speaker identification. The individual Gaussian components of a GMM are shown to represent some general speaker-dependent spectral shapes that are effective for modeling speaker identity. The focus of this work is on applications which require high identification rates using short utterance from unconstrained conversational speech and robustness to degradations produced by transmission over a telephone channel. A complete experimental evaluation of the Gaussian Mixture speaker model is conducted on a 49 speaker, conversational telephone speech database. The experiments examine algorithmic issues (initialization, variance limiting, model order selection), spectral variability robustness techniques, large population performance, and comparisons to other speaker modeling techniques (uni-modal Gaussian, VQ codebook, tied Gaussian Mixture, and radial basis functions). The Gaussian Mixture speaker model attains 96.8% identification accuracy using 5 second clean speech utterances and 80.8% accuracy using 15 second telephone speech utterances with a 49 speaker population and is shown to outperform the other speaker modeling techniques on an identical 16 speaker telephone speech task
Ariya Rastrow - One of the best experts on this subject based on the ideXlab platform.
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subspace Gaussian Mixture models for speech recognition
International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Daniel Povey, Lukas Burget, Mohit Agarwal, Pinar Akyazi, Kai Feng, Arnab Ghoshal, Ondrej Glembek, Nagendra Kumar Goel, Martin Karafiat, Ariya RastrowAbstract: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 subspace. This style of acoustic model allows for a much more compact representation and gives better results than a conventional modeling approach, particularly with smaller amounts of training data.