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Statistical speech models and a probabilistic technique called Gaussian mixture modelling are then used to identify each phoneme, before reconstructing the original word.
Latent growth mixture modelling was used to examine the course of fatigue over time.
Most algorithms handling mixture models were originally specifically designed for processing mixtures of Gaussians.
Gaussian mixture models were used to model each of the four classes.
Three mixture models were exploited for their potentials to model the sampling frequencies.
Gaussian mixture models were estimated in R with mixtools [ 71].
In this paper, mixture models were fit using the flexmix [ 16] R package.
Growth mixture models were applied to identify trajectory classes and their predictors.
Mixture models were fitted to the glucose values in each country to derive cut points for normal glycemia.
Mixture models were used to identify such animals (runts) based on their observed growth rates and sexual maturation.
In this study, P values for significance of the mixture model were based on a χ distribution with 4 d.f.
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