We present an experimental setup to evaluate the relative peiformance of single gaussian and mixture of gaussians models for skin color modeling. Firstly, a sample set of J, J 20, 000 skin pixels from a number of ethnic groups is selected and represented in the chromaticity space. In the following, parameter estimation for both the single gaussian and seven (with 2 to 8 gaussian components) gau...

Gaussian Mixture Models (GMMs) of power spectral densities of speech and noise are used with explicit Bayesian estimations in Wiener filtering of noisy speech. No assumption is made on the nature or stationarity of the noise. No voice activity detection (VAD) or any other means is employed to estimate the input SNR. The GMM mean vectors are used to form sets of over-determined system of equatio...

<p style='text-indent:20px;'>Despite the rapid development of computational hardware, treatment large and high dimensional data sets is still a challenging problem. The contribution this paper to topic twofold. First, we propose Gaussian mixture model in conjunction with reduction dimensionality each component by principal analysis, which call PCA-GMM. To learn (low dimensional) parameter...

This paper describes a new approach to acoustic mod-eling for large vocabulary continuous speech recognition (LVCSR) systems. Each phone is modeled with a large Gaussian mixture model (GMM) whose context-dependent mixture weights are estimated with a sentence-level discrim-inative training criterion. The estimation problem is casted in a neural network framework, which enables the incorporation...

Prior to publication, please maintain the enclosed paper in confidence and use it only for purposes of evaluating the merit of the proposed paper, and other activities reasonably related to the review process, and please do not make it available, in whole or in part, to the public. The authors thanks IEEE Transactions in Speech and Audio Processing for their courtesy and professionalism in this...

In this paper, the performance of Perceptual Linear Prediction (PLP) features has been compared with the performance of Linear Prediction Coefficient (LPC) features for speaker identification. Two classification techniques, Gaussian Mixture Models (GMM) and Vector Quantization (VQ) with Dynamic time wrapping (DTW) are used for classification of speakers based on their speech samples into respec...

In this paper we propose a new incremental estimation of Gaussian mixture models which can be used for applications of online learning. Our approach allows for adding new samples incrementally as well as removing parts of the mixture by the process of unlearning. Low complexity of the mixtures is maintained through a novel compression algorithm. In contrast to the existing approaches, our appro...

This paper is a pre-print of a paper that has been accepted for publication in the Proceedings of the 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016. The final publication is available at link.springer.com (http://link.springer.com/chapter/10.1007/978-3-319-31750-2 24). Abstract. We present new initialization methods for the expectationmaximization algorithm fo...

Gaussian Mixture Modeling (GMM) is a parametric method for high dimensional density estimation. Incremental learning of GMM is very important in problems such as clustering of streaming data and robot localization in dynamic environments. Traditional GMM estimation algorithms like EM Clustering tend to be computationally very intensive in these scenarios. We present an incremental GMM estimatio...