This study combines a Gaussian mixture model support vector machine (GMM-SVM) system with a nonlinear feature transformation, discriminatively trained to extract speaker specific features from MFCCs. Separation of the speaker information component and non-speaker related information in the speech signal is accomplished using a regularized siamese deep network (RSDN). RSDN learns a hidden repres...

This paper addresses the problem of robust speech recognition in noisy conditions in the framework of hidden Markov models (HMMs) and missing feature techniques. It presents a new statistical approach to detection and estimation of unreliable features based on a probabilistic measure and Gaussian mixture model (GMM). In the estimation process, the GMM is compensated using parameters of the stat...

This paper determines the properties of standard generalized method of moments (GMM) estimators, tests, and confidence sets (CSs) in moment condition models in which some parameters are unidentified or weakly identified in part of the parameter space. The asymptotic distributions of GMM estimators are established under a full range of drifting sequences of true parameters and distributions. The...

In large-scale multimedia event detection, complex target events are extracted from a large set of consumer-generated web videos taken in unconstrained environments. We devised a multimedia event detection method based on Gaussian mixture model (GMM) supervectors and support vector machines. A GMM supervector consists of the parameters of a GMM for the distribution of low-level features extract...

We compared recumbent bicycle kinetics in diabetic peripheral neuropathy and nondiabetic men (nine per group). 3D kinematic and force pedal data in a linked-segment model were used. The generalized muscle moment (GMM) patterns were similar between the two groups except for (1) decreased maximum knee flexor moment, (2) increased minimum knee flexor GMM, and (3) maximum hip extensor GMM by the di...

Emotion classification is essential for understanding human interactions and hence is an important module in HumanComputer Interaction (HCI) systems. Well-performed emotion classification systems have potential to integrate into the HCI systems to provide additional user state details. This paper presents an emotion classification system that employs Emotional Dissimilarity (ED) measure. Instea...

We compare two classifier approaches, namely classifiers based on Multi Layer Perceptrons (MLPs) and Gaussian Mixture Models (GMMs), for use in a face verification system. The comparison is carried out in terms of performance, robustness and practicability. Apart from structural differences, the two approaches use different training criteria; the MLP approach uses a discriminative criterion, wh...

Data-driven analysis methods, such as independent component analysis (ICA) and clustering, have found a fruitful application in the analysis of functional magnetic resonance imaging (fMRI) data for identifying functionally connected brain networks. Unlike the traditional regression-based hypothesis-driven analysis methods, the principal advantage of data-driven methods is their applicability to...

We develop a generalized method of moments (GMM) estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, one cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first-step consistent estimate. When the functional form of the underly...

A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimation of the classification rate. The estimation is done using the k-fold cross validation. In order to perform fast in terms of computing tim...