Dorra Ben Ayed Mezghanni

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This paper introduces and motivates the use of the statistical method Gaussian Mixture Model (GMM) and Support Vector Machines (SVM) for robust text-independent speaker identification. Features are extracted from the dialect DR1 of the Timit corpus. They are presented by MFCC, energy, Delta and Delta-Delta coefficients. GMM is used to model the feature(More)
Support vector machine (SVM) was the first proposed kernel-based method. It uses a kernel function to transform data from input space into a high-dimensional feature space in which it searches for a separating hyperplane. SVM aims to maximise the generalisation ability that depends on the empirical risk and the complexity of the machine. SVM has been widely(More)
— The purpose of speech emotion recognition system is to classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral and happiness. Speech features that are commonly used in speech emotion recognition (SER) rely on global utterance level prosodic features. In our work, we evaluate the impact of frame-level(More)
Several speaker identification systems are giving good performance with clean speech but are affected by the degradations introduced by noisy audio conditions. To deal with this problem, we investigate the use of complementary information at different levels for computing a combined match score for the unknown speaker. In this work, we observe the effect of(More)
The speech feature extraction has been a key focus in robust speech recognition research; it significantly affects the recognition performance. In this paper, we first study a set of different features extraction methods such as linear predictive coding (LPC), mel frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) with several(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)