This paper presents a review of various speaker verification approaches in realistic world, and explore a combinational approach between Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) as well as Gaussian Mixture Model (GMM) and Universal Background Model (UBM).

acoustic analysis is a proper method in vocal fold pathology diagnosis so that itcan complement and in some cases replace the other invasive, based on direct vocalfold observation, methods. there are different approaches and algorithms for vocalfold pathology diagnosis. these algorithms usually have three stages which arefeature extraction, feature reduction and classification. in this paper in...

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...

Recent studies have shown that speech recognizers may benefit from data in languages other than the target language through efficient acoustic modelor feature-level adaptation. Crosslingual Tandem-Subspace Gaussian Mixture Models (SGMM) are successfully able to combine acoustic modeland featurelevel adaptation techniques. More specifically, we focus on under-resourced languages (Afrikaans in ou...

In this paper, we propose two techniques to improve the acoustic model of a low-resource language by: (i) Pooling data from closely related languages using a phoneme mapping algorithm to build acoustic models like subspace Gaussian mixture model (SGMM), phone cluster adaptive training (Phone-CAT), deep neural network (DNN) and convolutional neural network (CNN). Using the low-resource language ...

We present an unsupervised classification algorithm based on an ICA mixture model. A mixture model is a model in which the observed data can be categorized into several mutually exclusive data classes. In an ICA mixture model, it is assumed that the data in each class are generated by a linear mixture of independent sources. The algorithm finds the independent sources and the mixing matrix for ...