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 ...

The present study evaluates MBCM and GMM solutions for both ASV and ASI problems involving text-independent telephone speech from the King speech database. The MBCM's accuracy is enhanced by selectively removing those classi ers within the model which perform worst (pruning). An unpruned MBCM outperforms a GMM for ASV and speakers taken from within the same dialectic region (San Diego, CA). Onc...

This paper examines GMM and ML estimation of econometric models and the theory of Hausman tests with sampling weights. Weighted conditional GMM can be more e$cient than weighted conditional MLE, an ine$cient alternative to full information MLE under choice-based sampling, unless regressions have homoscedastic additive disturbances or sampling weights are independent of exogenous variables. GMM ...

Point-of-Interest (POI) recommendation is a significant service for location-based social networks (LBSNs). It recommends new places such as clubs, restaurants, and coffee bars to users. Whether recommended locations meet users’ interests depends on three factors: user preference, social influence, and geographical influence. Hence extracting the information from users’ check-in records is the ...

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...