Rimah Amami

An incremental method combining density clustering and support vector machines for voice pathology detection

Incremental DBSCAN and SVM methods have been combined to classify pathological voice from normal voice.DBSCAN algorithm with an incremental learning has been used to detect noises.For each voices group (normal/pathological), we use a feature technique based on MFCC features.Incremental DBSCAN-SVM method has been built to incrementally detect noises, analyze and classify pathological voices. Display Omitted Machine learning techniques are a valuable tool for discriminative classification. They have been applied to a diverse range of applications in speech processing, such as the analysis of pathological voices. We propose, in this paper, a novel policy, called Incremental DBSCAN-SVM in order to detect noises, to analyze and to classify pathological voice from normal voice. We use a modified density-based clustering algorithm named DBSCAN with an incremental learning in order to detect noisy samples. Then, the output model is submitted to Support Vector Machines (SVM) classifier with a Radial Basis Function (RBF) kernel to discriminate between normal and pathological voices. Our method has the ability to handle incremental and dynamic voices database which evolve over time. We support our approach with empirical evaluation using voices data set from the Massachusetts Eye and Ear Infirmary Voice and Speech Laboratory (MEEI) database to show the effectiveness of our method in terms of detection voice disorders.

Adaboost with SVM using GMM supervector for imbalanced phoneme data

In machine learning, AdaBoost with Support vector Machines (SVM) based component classifier have shown to be a successful method for classification on balanced dataset with all classes having relatively similar distribution. However, the success of this method is limited when it is applied for imbalanced datasets. In many real applications, the classification of data with imbalanced proportions will be problematic since the algorithm can be biased and then might predict all the samples into majority classes. Many studies were conducted to overcome imbalance data problem by using hybrid algorithms. In this paper, we propose an improved AdaBoost with SVM based weak learner algorithm using Gaussian Mixture Modeling (GMM) supervectors called GSV-ADSVM. GMM supervectors are constructed applying MAP adaptation of the means of the mixture components based on speech from a target phoneme of TIMIT corpus. Those supervectors will be used as input datasets for the hybrid Adaboost-SVM. The main goal of this paper is to investigate the impact of using GMM supervectors with the boosted SVM in a multi-class phoneme recognition problem with the aim to advance the classification of imbalanced data since certain class of interest have very small size.

Incorporating Belief Function in SVM for Phoneme Recognition

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 phoneme in the data set for recognition problems may differ in the degree of significance due to noise, inaccuracies, or abnormal characteristics; All those problems can lead to the inaccuracies in the prediction phase. Unfortunately, the standard formulation of SVM does not take into account all those problems and, in particular, the variation in the speech input. This paper presents a new formulation of SVM (B-SVM) that attributes to each phoneme a confidence degree computed based on its geometric position in the space. Then, this degree is used in order to strengthen the class membership of the tested phoneme. Hence, we introduce a reformulation of the standard SVM that incorporates the degree of belief. Experimental performance on TIMIT database shows the effectiveness of the proposed method B-SVM on a phoneme recognition problem.

Robust Noisy Speech Recognition Using Deep Neural Support Vector Machines

This paper aims to classify noisy sound samples in several daily indoor and outdoor acoustic scenes using an optimized deep neural networks (DNNs). The advantage of a traditional DNNs lies in using at the top layer a softmax activation function which is a logistic regression in order to learn the output label in a multi-class recognition problem. In this paper, we optimize the DNNs by replacing the softmax activation function by a linear support vector machine.

Possibilistic support vector machines for automatic phoneme recognition

We propose, in this paper, new support vector machines (SVM) formulation that incorporates possibilistic weights based upon the geometric distribution of the phonemes 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 the possibilitic weights (PossSVM). The experimental results show a greater performance of the proposed method than the existing SVM in the phoneme recognition task. Moreover, in this paper we tested several possibilistic distances in aim to find the most suitable with our data sets.

Automatic emotion recognition using generative and discriminative classifiers in the GMM mean space

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 study and evaluate the combination of GMM approach with different generative models (K-nearest neighbors, Naive Bayes, Multilayer perceptron) and discriminative models (Support Vector Machine, Decision Trees) in the setting of a robust emotion recognition system. We illustrate this framework using Mel-frequency cepstral coefficients and Sequential Forward Selection method applicable to GMM supervectors.

Robust speech recognition using consensus function based on multi-layer networks

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 final partition from different clustering results requires skillfulness and robustness of the classification algorithm. In addition, the major problem with the consensus function is its sensitivity to the used data sets quality. This limitation is due to the existence of noisy, silence or redundant data. This paper proposes a novel consensus function of cluster ensembles based on Multilayer networks technique and a maintenance database method. This maintenance database approach is used in order to handle any given noisy speech and, thus, to guarantee the quality of databases. This can generates good results and efficient data partitions. To show its effectiveness, we support our strategy with empirical evaluation using distorted speech from Aurora speech databases.

Feature Selection Using Adaboost for Phoneme Recognition

The propose to improve a Support Vector Machines (SVM) learning accuracy by using a Real Adaboost algorithm for selecting features is presented in the chapter. This technique aims to minimize the recognition error rates and the computational effort. Hence, the Real Adaboost will be used not as classifier but as a technique for selecting features in order to keep only the relevant features that will be used to improve our systems accuracy. Since the Real Adaboost is only used for binary classifications problems, we investigate different ways of combining selected features applied to a multi-class classification task. To experiment this selection, we use the phoneme datasets from TIMIT corpus [Massachusetts Institute of Technology (MIT), SRI International and Texas Instruments, Inc. (TI)] and Mel-Frequency Cepstral Coefficients (MFCC) feature representations. It must be pointed out that before using the Real Adaboost the multi-class phoneme recognition problem should be converted into a binary one.

The challenges of SVM optimization using Adaboost on a phoneme recognition problem

The use of digital technology is growing at a very fast pace which led to the emergence of systems based on the cognitive infocommunications. The expansion of this sector impose the use of combining methods in order to ensure the robustness in cognitive systems.