In this paper, we propose a novel boosted mixture learning (BML) framework for Gaussian mixture HMMs in speech recognition. BML is an incremental method to learn mixture models for classification problem. In each step of BML, one new mixture component is calculated according to functional gradient of an objective function to ensure that it is added along the direction to maximize the objective ...

The primary goal of this master thesis project is to implement a text independent speaker verification module for GIVES. Secondary goals are to implement a fast scoring method and compare performance between the implemented text independent module and an available text dependent module. The project also includes a literature study. The text independent module is based on adapted Gaussian Mixtur...

A new method has been developed to approximate one Gaussian mixture by another in a process that generalizes the idea of importance re-sampling in a particle filter. This algorithm is being developed as part of an effort to generalize the concept of a particle filter. In a traditional particle filter, the underlying probability density function is described by particles: Dirac delta functions w...

This technical report presents a novel algorithm for unsupervised clustering of functions. It proceeds by developing the theory of unsupervised classification in mixtures from the familiar mixture of Gaussian distributions, to the infinite mixture of Gaussian processes. At each stage a both a theoretical and an algorithmic exposition are presented. We consider unsupervised classification (or cl...

By a “covering” we mean a Gaussian mixture model fit to observed data. Approximations of the Bayes factor can be availed of to judge model fit to the data within a given Gaussian mixture model. Between families of Gaussian mixture models, we propose the Rényi quadratic entropy as an excellent and tractable model comparison framework. We exemplify this using the segmentation of an MRI image volu...

We present a novel method for representing “extruded” distributions. An extruded distribution is an M -dimensional manifold in the parameter space of the component distribution. Representations of that manifold are “continuous mixture models”. We present a method for forming one-dimensional continuous Gaussian mixture models of sampled extruded Gaussian distributions via ridges of goodness-of-#...

This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation. Two Gaussian mixture model based algorithms are proposed to effectively preserve image details and avoids image degradation. Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select proc...

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...

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...

Despite all advances in the speaker recognition domain, Gaussian Mixture Models (GMM) remain the state-of-the-art modeling technique in speaker recognition systems. The key idea is to approximate the probability density function ( ) of the feature vectors associated to a speaker with a weighted sum of Gaussian densities. Although the extremely efficient Expectation-Maximization (EM) algorithm c...