Graphical Models for Multi-dialect Arabic Isolated Words Recognition

AbstractThis paper presents the use of multiple hybrid systems for the recognition of isolated words from a large multi-dialect Arabic vocabulary. Such as the Hidden Markov models (HMM), Dynamic Bayesian networks (DBN) lack a discriminatory ability especially on speech recognition even if their progress is huge. Multi-Layer perceptrons (MLP) was applied in literature as an estimator of emission probabilities in HMM and proves it effectiveness. In order to ameliorate the results of recognition systems, we apply Support Vectors Machine (SVM) as an estimator of posterior probabilities since they are characterized by a high predictive power and discrimination. Moreover, they are based on a structural risk minimization (SRM) where the aim is to set up a classifier that minimizes a bound on the expected risk, rather than the empirical risk. In this work we have done a comparative study between three hybrid systems MLP/HMM, SVM/HMM and SVM/DBN and the standards models of HMM and DBN. In this paper, we describe the use of the hybrid model SVM/DBN for multi-dialect Arabic isolated words recognition. So, by using 67,132 speech files of Arabic isolated words, this work arises a comparative study of our acknowledgment system of it as the following: the use of especially the HMM standards leads to a recognition rate of 74.18%.as the average rate of 8 domains for everyone of the 4 dialects. Also, with the hybrid systems MLP/HMM and SVM/HMM we succeed in achieving the value of 77.74%.and 7806% respectively. Moreover, our proposed system SVM/DBN realizes the best performances, whereby, we achieve 87.67% as a recognition rate more than 83.01% obtained by GMM/DBN

Particle swarm optimization for support vector clustering Separating hyper-plane of unlabeled data

International audienceThe objective of this work is to design a new method to solve the problem of integrating the Vapnik theory, as regards support vector machines, in the field of clustering data. For this we turned to bio-inspired meta-heuristics. Bio-inspired approaches aim to develop models resolving a class of problems by drawing on patterns of behavior developed in ethology. For instance, the Particle Swarm Optimization (PSO) is one of the latest and widely used methods in this regard. Inspired by this paradigm we propose a new method for clustering. The proposed method PSvmC ensures the best separation of the unlabeled data sets into two groups. It aims specifically to explore the basic principles of SVM and to combine it with the meta-heuristic of particle swarm optimization to resolve the clustering problem. Indeed, it makes a contribution in the field of analysis of multivariate data. Obtained results present groups as homogeneous as possible. Indeed, the intra-class value is more efficient when comparing it to those obtained by Hierarchical clustering, Simple K-means and EM algorithms for different database of benchmark

Keyword Spotting using Support Vector Machines

Colloque avec actes et comité de lecture. internationale.International audienceSupport Vector Machines is a new and promising technique in statistical learning theory. Recently, this technique produced very interesting results in pattern recognition. In this paper, one of the first application of Support Vector Machines (SVM) technique for the problem of keyword spotting is presented. It classifies the correct and the incorrect keywords by using linear and Radial Basis Function kernels. This is a first work proposed to use SVM in keyword spotting, in order to improve recognition and rejection accuracy. The obtained results are very promising

Support Vector Machines for Keyword Spotting

Colloque avec actes et comité de lecture. internationale.International audienceSupport Vector Machines is a new and promising technique in statistical learning theory. Recently, this technique produced very interesting results in pattern recognition. In this paper, one of the first application of Support Vector Machines(SVM) technique for the problem of keyword spotting is presented. A two-class SVM approach is first proposed which classifies the correct and the incorrect keywords. The second method uses multi-class SVM, it assigns to each keyword a class label This is a first work proposed to use two-class SVM and multi-class SVin keyword spotting, in order to improve recognition accuracy. The results obtained are very promising

Recognition and Rejection Performance in Wordspotting Systems Using Support Vector Machines

Colloque avec actes et comité de lecture. internationale.International audienceSupport Vector Machines (SVM) is one such machine learning technique that learns the decision surface through a process of discrimination and has a good generalization capacity. SVMs have been proven to be successful classifiers on several classical pattern recogntion problems. In this paper, one of the first applications of Support Vector Machines (SVM) technique for the problem of keyword spotting is presented. It classifies the correct and the incorrect keywords by using linear and Radial Basis Function kernels. This is a first work proposed to use SVM in keyword spotting in order to improve recognition and rejection accuracy. The obtained results are very promising. The Equal Error Rate (EER) for the linear kernel is about 16,34\% compared to 15,23\% obtained by the radial basis function kernel

A New Keyword Spotting Approach Based on Reward Function

Colloque avec actes et comité de lecture. nationale.National audienceIn this paper, we compare the performance achieved by different word-spotting techniques based on hidden Markov models. We propose two methods to detect keywords, the first one uses a GMM (Gaussian Mixture Model) as a filler model to absorb the out-of-vocabulary words. The second is an alternative approach which does not attempt to model out-of-vocabulary words, instead, it uses buckled phonemes basedgrammar. Furthermore, it uses different reward functions to favourite the recognition of the keywords phonemes

Recognition and Rejection Performance in Wordspotting Systems Using Hidden Markov modeling techniques

Colloque avec actes et comité de lecture. internationale.International audienceThis paper deals with the problem of acceptance/rejection of recognition hypotheses for continuous speech utterances. Two different techniques are investigated to improve the rejection of out-of-vocabulary (OOV) words. A combined approach is first proposed which uses two garbage models (a trained one and an on-line garbage model). The second method uses the trained garbage model and consists in post-processing the recognizer hypotheses by computing for each of them a confidence measure. Both approaches are evaluated in the context of a stock exchange application through the telephone for French. The parameters of the two approaches are studied to improve recognition accuracy

Comparaison de différentes méthodes de classification pour la détection de mots clés en parole continue

Colloque avec actes et comité de lecture. internationale.International audienceCet article s'inscrit dans le cadre de la détection de mots clés dans un flux de parole. Nous présentons le problème de détection comme un problème de classification où chaque mot clé peut appartenir à deux classes différentes, à savoir ``correct'' et ``incorrect''. Cette classification est réalisée tout d'abord, par l'utilisation des Réseaux de Neurones Artificiels (RNA) en particulier le Perceptron Multi-Couches (PMC). Ensuite, nous proposons l'utilisation des SVM comme technique de classification innovante et efficace et qui a fait ses preuves dans plusieurs domaines de recherche. Chaque mot clé reconnu est représenté par un vecteur caractéristique qui constitue l'entrée du classifieur. Pour déterminer ce vecteur, nous proposons trois représentations vectorielles basées sur l'emploi des probabilités d'observations acoustiques locales et de la durée de chaque éta

Support Vector Machines for Keyword Spotting

Colloque avec actes et comité de lecture. internationale.International audienceSupport Vector Machines is a new and promising technique in statistical learning theory. Recently, this technique produced very interesting results in pattern recognition. In this paper, one of the first application of Support Vector Machines(SVM) technique for the problem of keyword spotting is presented. A two-class SVM approach is first proposed which classifies the correct and the incorrect keywords. The second method uses multi-class SVM, it assigns to each keyword a class label This is a first work proposed to use two-class SVM and multi-class SVin keyword spotting, in order to improve recognition accuracy. The results obtained are very promising