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 Models (GMMs) in combination with Support Vector Machine (SVM) classifiers have been shown to give excellent classification accuracy in speaker recognition. In this work we use this approach for language identification, and we compare its performance with the standard approach based on GMMs. In the GMM-SVM framework, a GMM is trained for each training or test utterance. Since i...

Definition A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system, such as vocal-tract related spectral features in a speaker recognition system. GMM parameters are estimated ...

Mixture models, such as Gaussian Mixture Model, have been widely used in many applications for modeling data. Gaussian mixture model (GMM) assumes that data points are generated from a set of Gaussian models with the same set of mixture weights. A natural extension of GMM is the probabilistic latent semantic analysis (PLSA) model, which assigns different mixture weights for each data point. Thu...

automatic segmentation of multiple sclerosis (ms) lesions in brain magnetic resonance imaging (mri) has been widely investigated in the recent years with the goal of helping ms diagnosis and patient follow‑up. in this research work, gaussian mixture model (gmm) has been used to segment the ms lesions in mris, including t1‑weighted (t1‑w), t2‑w, and t2‑fluid attenuation inversion recovery. usual...

Gaussian Mixture Models (GMM) have been broadly applied for the fitting of probability density function. However, due to the intrinsic linearity of GMM, usually many components are needed to appropriately fit the data distribution, when there are curve manifolds in the data cloud. In order to solve this problem and represent data with curve manifolds better, in this paper we propose a new nonli...

speaker verification is the process of accepting or rejecting claimed identity in terms of its sound features. a speaker verification system can be used for numerous security systems, including bank account accessing, getting to security points, criminology and etc. when a speaker verification system wants to check the identity of individuals remotely, it confronts problems such as noise effect...

In this work we propose to address the problem of non-frontal face verification when only a frontal training image is available (e.g. a passport photograph) by augmenting a client’s frontal face model with artificially synthesized models for non-frontal views. In the framework of a Gaussian Mixture Model (GMM) based classifier, two techniques are proposed for the synthesis: UBMdiff and LinReg. ...

The Gaussian Mixture Model Universal Background Model (GMM-UBM) system is one of the predominant approaches for text-independent speaker verification, because both the target speaker model and the impostor model (UBM) have generalization ability to handle “unseen” acoustic patterns. However, since GMM-UBM uses a common anti-model, namely UBM, for all target speakers, it tends to be weak in reje...

It is shown that e¢ cient GMM (generalized method of moments) estimation of a linear model corresponds to standard IV (instrumental variables) estimation of this model, after transforming it such (as in GLS) that its resulting disturbances have a scalar covariance matrix, while using as instruments the original instruments linearly transformed by the transpose of the inverse of the matrix used ...