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...

This paper describes the voice conversion based on the Mixtures of Factor Analyzers (MFA) which can provide an efficient modeling with a limited amount of training data. As a typical spectral conversion method, a mapping algorithm based on the Gaussian Mixture Model (GMM) has been proposed. In this method two kinds of covariance matrix structures are often used : the diagonal and full covarianc...

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...

This paper presents an interval version of the Geometric Machine Model (GMM) and the programming language induced by its structure. The GMM is an abstract machine model, based on Girard’s coherence space, capable of modelling sequential, alternative, parallel (synchronous) and non-deterministic computations on a (possibly infinite) shared memory. The processes of the GMM are inductively constru...

To overcome the defects of common used algorithms based on model for abnormal speech recognition, which existed insufficient training data and difficult to fit each type of abnormal characters, an abnormal speech detection method based on GMM-UBM was proposed in this paper. For compensating the defects of methods based on model which difficult to deal with the diversification speech. Firstly, m...

A new approach to face verification from 3D data is presented. The method uses 3D registration techniques designed to work with resolution levels typical of the irregular point cloud representations provided by Structured Light scanning. Preprocessing using a-priori information of the human face and the Iterative Closest Point algorithm are employed to establish correspondence between test and ...

We extend the mass-constraint data clustering and vector quantization algorithm to estimate Gaussian Mixture Models (GMMs) as image features applying to the image retrieval problems. The GMM feature is an alternative method to histograms to represent data density distributions. Histograms are well known for their advantages including rotation invariance, low calculation load, and so on. The GMM...