In this paper, we proposed an unsupervised posture modeling method based on Gaussian Mixture Model (GMM). Specifically, each learning posture is described based on its movement features by a set of spatial-temporal interest points (STIPs), salient postures are then clustered from these training samples by an unsupervised algorithm, here we give the comparison of four candidate classification me...

Recent advances in the econometric modelling of count data have often been based on the generalized method of moments (GMM). However, the two-step GMM procedure may perform poorly in small samples, and several empirical likelihood-based estimators have been suggested alternatively. In this paper I discuss empirical likelihood (EL) estimation for count data models with endogenous regressors. I c...

We propose a person verification method using behavioral patterns of human upper body motion. Behavioral patterns are represented by three-dimensional features obtained from a time-of-flight camera. We take a statistical approach to model the behavioral patterns using Gaussian mixture models (GMM) and support vector machines. We employ the maximum likelihood linear regression adaptation method ...

An effective method based on GMM is proposed in this paper for speech emotional recognition; a compensation transformation is introduced in the recognition stage to reduce the influence of variations in speech characteristics and noise. The extraction of emotional features includes the globe feature, time series structure feature, LPCC, MFCC and PLP. Five human emotions (happiness, angry, surpr...

The paper focuses on two methods of evaluation of successfulness of speech signal enhancement recorded in the open-air magnetic resonance imager during phonation for the 3D human vocal tract modeling. The first approach enables to obtain a comparison based on statistical analysis by ANOVA and hypothesis tests. The second method is based on classification by Gaussian mixture models (GMM). The pe...

This paper introduces a pronunciation verification method to be used in an automatic assessment therapy tool of child disordered speech. The proposed method creates a phonebased search lattice that is flexible enough to cover all probable mispronunciations. This allows us to verify the correctness of the pronunciation and detect the incorrect phonemes produced by the child. We compare between t...

Automatic segmentation of multiple sclerosis (MS) lesions in brain magnetic resonance imaging (MRI) has been widely investigated in the recent years with the goal of helping MS diagnosis and patient follow-up. In this research work, Gaussian mixture model (GMM) has been used to segment the MS lesions in MRIs, including T1-weighted (T1-w), T2-w, and T2-fluid attenuation inversion recovery. Usual...

11 THE GMM ESTIMATION 2 11.1 Consistency and Asymptotic Normality . . . . . . . . . . . . . . . . . . . . . 3 11.2 Regularity Conditions and Identification . . . . . . . . . . . . . . . . . . . . . 4 11.3 The GMM Interpretation of the OLS Estimation . . . . . . . . . . . . . . . . . 5 11.4 The GMM Interpretation of the MLE . . . . . . . . . . . . . . . . . . . . . . . 6 11.5 The GMM Estimation ...

It is well-known that the performance of acoustic-to-articulatory inversion improves by smoothing the articulatory trajectories estimated using Gaussian mixture model (GMM) mapping (denoted by GMM + Smoothing). GMM + Smoothing also provides similar performance with GMM mapping using dynamic features, which integrates smoothing directly in the mapping criterion. Due to the separation between smo...