We show that a classifier based on Gaussian mixture models (GMM) can be trained discriminatively to improve accuracy. We describe a training procedure based on the extended Baum-Welch algorithm used in speech recognition. We also compare the accuracy and degree of sparsity of the new discriminative GMM classifier with those of generative GMM classifiers, and of kernel classifiers, such as suppo...

The paper addresses a novel algorithm for speaker searching and indexation based on unsupervised GMM training. The proposed method doesn’t require a predefined set of generic background models, and the GMM speaker models are trained only from test samples. The constrain of the method is that the number of the speakers has to be known in advance. The results of initial experiments show that the ...

This study is mainly focusing on the problem of spacecraft close-range proximity with obstacle avoidance in presence complex shape. A novel Gaussian mixture model–based nonsingular terminal sliding mode control (GMM-NTSMC) proposed. achieved by developing GMM-based potential function a switching surface NTSMC. It theoretically proved that closed-loop system globally stable. The main contributio...

Gaussian mixture models (GMM) and support vector machines (SVM) are introduced to classify faults in a population of cylindrical shells. The proposed procedures are tested on a population of 20 cylindrical shells and their performance is compared to the procedure, which uses multi-layer perceptrons (MLP). The modal properties extracted from vibration data are used to train the GMM, SVM and MLP....

In this paper, methods of Gaussian Mixture Model (GMM) are presented for both silence/voiced/voiceless segmentation and tone decision in Mandarin continuous speech recognition system. GMM has been used for silence/voiced/voiceless segmentation before, but the feature parameters can be modified to improve both accuracy and speed. As a popular method in pattern recognition, GMM is first proposed ...