This study focuses on the classification of pathological voice using GMM (Gaussian Mixture Model) and compares the results to the previous work which was done by ANN (Artificial Neural Network). Speech data from normal people and patients were collected, then diagnosed and classified into two different categories. Six characteristic parameters (Jitter, Shimmer, NHR, SPI, APQ and RAP) were chose...

This paper presents a Gaussian Mixture Model (GMM) to identify the script of handwritten words of Roman, Devanagari, Kannada and Telugu scripts. It emphasizes the significance of directional energies for identification of script of the word. It is robust to varied image sizes and different styles of writing. A GMM is modeled using a set of six novel features derived from directional energy dist...

This paper describes the Loquendo – Politecnico di Torino system evaluated on the 2006 NIST speaker recognition evaluation dataset. This system was among the best participants in this evaluation. It combines the results of two independent GMM systems: a Phonetic GMM and a classical GMM. Both systems rely on an intersession variation compensation approach, performed in the feature domain. It all...

This paper introduces and motivates the use of the statistical method Gaussian Mixture Model (GMM) and Support Vector Machines (SVM) for robust textindependent speaker identification. Features are extracted from the dialect DR1 of the Timit corpus. They are presented by MFCC, energy, Delta and Delta-Delta coefficients. GMM is used to model the feature extractor of the input speech signal and SV...

An electrostatic model based on charge density is proposed as a model for future force fields. The model is composed of a nucleus and a single Slater-type contracted Gaussian multipole charge density on each atom. The Gaussian multipoles are fit to the electrostatic potential (ESP) calculated at the B3LYP/6-31G* and HF/aug-cc-pVTZ levels of theory and tested by comparing electrostatic dimer ene...

In this short document, we derive a tree-independent single-tree algorithm for Gaussian mixture model training, based on a technique proposed by Moore [8]. Here, the solution we provide is tree-independent and thus will work with any type of tree and any type of traversal; this is more general than Moore’s original formulation, which was limited to mrkd-trees. This allows us to develop a flexib...

We generalize the classical max-min rate allocation policy with the support of the minimum rate requirement and peak rate constraint for each connection. Since a centralized algorithm for the generalized maxmin (GMM) rate allocation requires global information, which is difficult to maintain and manage in a large network, we develop a distributed protocol to achieve the GMM policy using the ava...