We propose a novel method to estimate the quality of coded speech signals. The joint probability distribution of the subjective mean opinion score (MOS) and perceptual distortion feature variables is modelled using a Gaussian mixture density. The feature variables are sifted from a large pool of candidate features using statistical data mining techniques. We study what combinations of features ...

Theories of learning and generalization hold that the generalization bias, defined as the difference between the training error and the generalization error, increases on average with the number of adaptive parameters. This article, however, shows that this general tendency is violated for a gaussian mixture model. For temperatures just below the first symmetry breaking point, the effective num...

At the era of digital revolution, social media data are growing at an explosive speed. Thanks to the prevailing popularity of mobile devices with cheap costs and high resolutions as well as the ubiquitous Internet access provided by mobile carriers, Wi-Fi, etc., numerous numbers of videos and pictures are generated and uploaded to social media websites such as Facebook, Flickr, and Twitter ever...

Imputation is a widely used method for handling missing data. It consists in the replacement of missing values with plausible ones. Parametric and nonparametric techniques are generally adopted for modelling incomplete data. Both of them have advantages and drawbacks. Parametric techniques are parsimonious but depend on the model assumed, while nonparametric techniques are more flexible but req...

The main objective of this paper is to identify themes from dialogues of telephone conversations in a real-life customer care service. In order to capture significant semantic content in spite of high expression variability, features are extracted in a large number of hidden spaces constructed with a Latent Dirichlet Allocation (LDA) approach. Multiple views of a spoke document can then be repr...

We present an optimal mass transport framework on the space of Gaussian mixture models, which are widely used in statistical inference. Our method leads to a natural way to compare, interpolate and average Gaussian mixture models. Basically, we study such models on a certain submanifold of probability densities with certain structure. Different aspects of this framework are discussed and severa...

We take a new look at parameter estimation for Gaussian Mixture Models (GMMs). In particular, we propose using Riemannian manifold optimization as a powerful counterpart to Expectation Maximization (EM). An out-of-the-box invocation of manifold optimization, however, fails spectacularly: it converges to the same solution but vastly slower. Driven by intuition from manifold convexity, we then pr...

Abstract. Recently stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. Also image segmentation means to divide one picture into different types of classes or regions, for example a picture of geometric shapes has some classes with different colors such as ’circle’, ’rectangle’, ’triangle’ and so ...