Rimah Amami

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In this paper, we have analyzed the impact of confusions on the robustness of phoneme recognitions system. The confusions are detected at the pronunciation and the confusions matrices of the phoneme recognizer. The confusions show that some similarities between phonemes at the pronunciation affect significantly the recognition rates. This paper proposes to(More)
It is known that the classification performance of Support Vector Machine (SVM) can be conveniently affected by the different parameters of the kernel tricks and the regularization parameter, C. Thus, in this article, we propose a study in order to find the suitable kernel with which SVM may achieve good generalization performance as well as the parameters(More)
In this article, we conduct a study on the performance of some supervised learning algorithms for vowel recognition. This study aims to compare the accuracy of each algorithm. Thus, we present an empirical comparison between five supervised learning classifiers and two combined classifiers: SVM, KNN, Naive Bayes, Quadratic Bayes Normal (QDC) and Nearst(More)
The Support Vector Machine (SVM)method has been widely used in numerous classification tasks. The main idea of this algorithm is based on the principle of the margin maximization to find an hyperplane which separates the data into two different classes.In this paper, SVM is applied to phoneme recognition task. However, in many real-world problems, each(More)
Recognizing human emotions is the indispensable requirement for efficient human machine interaction. Besides human facial expressions, speech is one of the latest challenges in automatic recognition of emotions. Current approaches in automatic speaker recognition systems are partly to entirely based on Gaussian mixture models (GMM). In this research, we(More)
We propose, in this paper, new support vector machines (SVM) formulation that incorporates possibilistic weights based upon the geometric distribution of the phoneme's data set input to the recognition system. Those possibilistic weights are computed based on a possibilistic distance. Hence, we introduce a new formulation of the standard SVM incorporating(More)
The clustering ensembles mingle numerous partitions of a specified data into a single clustering solution. Clustering ensemble has emerged as a potent approach for ameliorating both the forcefulness and the stability of unsupervised classification results. One of the major problems in clustering ensembles is to find the best consensus function. Finding(More)