This thesis devises quantization and source-channel coding schemes to increase the error robustness of the newly standardized ITU-T G.711.1 speech coder. The schemes employ Gaussian mixture model (GMM) based multiple description quantizers (MDQ). The thesis reviews the literature focusing on GMM based quantization, MDQ, and GMM-MDQ design methods and bit allocation schemes. GMM-MDQ are then des...

Many image modeling and segmentation problems have been tackled using Gaussian Mixture Models (GMM). The two most important issues in image modeling using GMMs is the selection of the appropriate low level features and the specification of the appropriate number of GMM components. In this work we deal with the second issue and present an approach for GMM-based image modeling employing an increm...

This paper considers first-order autoregressive panel model which is a simple model for dynamic panel data (DPD) models. The generalized method of moments (GMM) gives efficient estimators for these models. This efficiency is affected by the choice of the weighting matrix which has been used in GMM estimation. The non-optimal weighting matrices have been used in the conventional GMM estimators. ...

This entry describes the basic framework for statistical estimation and inference using Generalized Method of Moments and also illustrates the types of empirical models in finance to which these techniques have been applied. GeneralizedMethod of Moments (GMM) provides a computationally convenientmethod of obtaining consistent and asymptotically normally distributed estimators of the parameters ...

Fitting a Gaussian mixture model (GMM) to the smoothed speech spectrum allows an alternative set of features to be extracted from the speech signal. These features have been shown to possess information complementary to the standard MFCC parameterisation. This paper further investigates the use of these GMM features in combination with MFCCs. The extraction and use of a confidence metric to com...

Gaussian mixture model (GMM) has been widely used for data analysis in various domains including text documents, face images and genes. GMM can be viewed as a simple linear superposition of Gaussian components, each of which represents a data cluster. Recent models, namely Laplacian regularized GMM (LapGMM) and locally consistent GMM (LCGMM) have been proposed to preserve the than the original ...

To realize a robust spoken dialogue system for use in a real environment, the robust rejection of unintended inputs such as laughter, coughing, background speech and other noise based on GMM is implemented and examined on the basis of actual utterances. All the triggered inputs to a speech-oriented guidance system from 125 days of field tests in a public space are collected, and the occurrence ...

This paper presents a novel automatic speaker age and gender identification approach which combines five different methods at the acoustic level to improve the baseline performance. The five subsystems are (1) Gaussian mixture model (GMM) system based on mel-frequency cepstral coefficient (MFCC) features, (2) Support vector machine (SVM) based on GMM mean supervectors, (3) SVM based on GMM maxi...

The ability to monitor cognitive load level in real time is extremely useful for preventing fatal operating errors or improving the efficiency of task execution. In top of the success of our previously proposed speech based cognitive load monitoring system, we explored alternative classification techniques in this paper, including simple linear kernel Support Vector Machine (SVM), hybrid SVM-GM...

The purpose of the present study was to investigate and compare the segmentation ability of the Gaussian Mixture Models (GMM) against the Seeded Region Growing (SRG) methods in microarray spots segmentation. A simulated microarray image, each containing 200 spots, was produced. An automatic gridding process was developed in MATLAB and it was applied on the images for identifying the centers of ...