In this paper, a discriminative training procedure for a Gaussian Mixture Model (GMM) language identification system is described. The proposal is based on the Generalized Probabilistic Descent (GPD) algorithm and Minimum Classification Error Rates formulated to estimate the GMM parameters. The evaluation is conducted using the OGI multi-language telephone speech corpus. The experimental result...

Subspace Gaussian mixture model(GMM) is an alternative approach to approximate the probabilistic density function (p.d.f) of a set of independent identical distributed (i.i.d) data with prior density estimates. In this approach, the prior density of GMM parameters is estimated from a development dataset, and when predict the new enrolled data, the prior knowledge can be utilised by criteria lik...

This paper shows that Hidden Markov Models (HMMs) can be effectively applied to 3D face data. The examined HMM techniques are shown to be superior to a previously examined Gaussian Mixture Model (GMM) technique. Experiments conducted on the Face Recognition Grand Challenge database show that the Equal Error Rate can be reduced from 0.88% for the GMM technique to 0.36% for the best HMM approach.

OBJECTIVES A gaussian mixture model (GMM) was recently developed for estimating the probability density function of blood flow velocity measured with transcranial Doppler ultrasound data. In turn, the quantiles of the probability density function allow one to construct estimators of the "maximum" blood flow velocity. However, GMMs assume gaussianity, a feature that is not omnipresent in observe...

We develop a method for discovering the latent structure in MFCC feature data using the Hierarchical Dirichlet Process (HDP). Based on this structure, we compute timbral similarity between recorded songs. The HDP is a nonparametric Bayesian model. Like the Gaussian Mixture Model (GMM), it represents each song as a mixture of some number of multivariate Gaussian distributions However, the number...

Timbral modeling is fundamental in content based music similarity systems. It is usually achieved by modeling the short term features by a Gaussian Model (GM) or Gaussian Mixture Models (GMM). In this article we propose to achieve this goal by using the GMM-supervector approach. This method allows to represent complex statistical models by an Euclidean vector. Experiments performed for the musi...

This study proposes an acoustic model adaptation scheme to improve speech recognition in severely adverse environments utilizing untranscribed data. In the proposed method, a clean GMM is estimated from clean training data, and a noisecorrupted GMM is obtained by MAP adaptation over the adaptation data. The Gaussian component of the adapted HMMs is obtained using the transform of the most simil...

In this paper, we demonstrate how the differential Earth Mover’s Distance (EMD) may be used for visual tracking in synergy with Gaussian mixtures models (GMM). According to our model, motion between adjacent frames results in variations of the mixing proportions of the Gaussian components representing the object to be tracked. These variations are computed in closed form by minimizing the diffe...

In this work, a splitting and weighting scheme that allows for splitting a Gaussian density into a Gaussian mixture density (GMM) is extended to allow the mixture components to be arranged along arbitrary directions. The parameters of the Gaussian mixture are chosen such that the GMM and the original Gaussian still exhibit equal central moments up to an order of four. The resulting mixtures’ co...