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...

Image segmentation techniques are considered the main artifact against which the computer can visualize the objects in that scene and for further processing, many hand segmentation techniques are adopted in this direction which consumes the color cue as the enjoinder tools for spotting the skin pixels and non-skin pixels, GMM has been implemented successfully in this area which can tone single ...

We develop a GMM estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, once 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 underlying distribution is unknown, we...

The problem of context recognition from mobile audio data is considered. We consider ten different audio contexts (such as car, bus, office and outdoors) prevalent in daily life situations. We choose mel-frequency cepstral coefficient (MFCC) parametrization and present an extensive comparison of six different classifiers: knearest neighbor (kNN), vector quantization (VQ), Gaussian mixture model...

Speaker indexing has recently emerged as an important task due to the rapidly growing volume of audio archives. Current filtration techniques still suffer from problems both in accuracy and efficiency. In this paper an efficient method to simulate GMM scoring is presented. Simulation is done by fitting a GMM not only to every target speaker but also to every test utterance, and then computing t...

This paper presents adaptive voice-quality control methods based on one-to-many eigenvoice conversion. To intuitively control the converted voice quality by manipulating a small number of control parameters, a multiple regression Gaussian mixture model (MR-GMM) has been proposed. The MR-GMM also allows us to estimate the optimum control parameters if target speech samples are available. However...

Weak instruments arise when the instruments in linear IV regression are weakly correlated with the included endogenous variables. In nonlinear GMM, weak instruments correspond to weak identification of some or all of the unknown parameters. Weak identification leads to non-normal distributions, even in large samples, so that conventional IV or GMM inferences are misleading. Fortunately, various...

ExpEnd is a Gauss programme for non-linear generalised method of moments (GMM) estimation of exponential models with endogenous regressors for cross section and panel data. The estimators included in this package are simple Poisson pseudo ML; GMM for cross section data using moment conditions based on multiplicative or additive errors; within groups fixed effects Poisson for panel data; GMM est...

A novel idea of using giant magnetostrictive material (GMM) based actuators for journal bearing control is presented in this paper. Frequency response tests on GMM actuators and a journal bearing system were examined. The performances of the system running at various journal shaft rotational speeds under control were investigated. With the aid of GMM actuators, the performances on journal shaft...

A maximum likelihood parameter trajectory estimation based on a Gaussian mixture model (GMM) has been successfully implemented for acoustic-to-articulatory inversion mapping. In the conventional method, GMM parameters are optimized by maximizing a likelihood function for joint static and dynamic features of acoustic-articulatory data, and then, the articulatory parameter trajectories are estima...