Modélisation d'une situation d'apprentissage en termes de connaissances et de règles pour rendre compte de l'activité de l'élève: Etude dans le contexte de l'apprentissage de la lecture en classe

International audienceIn this article, we note that an activity cannot be only defined by its effective form and knowledge of the field concerned which is brought into play. We propose to define an activity by knowledge of the field of training brought into play, by its form but also in what it can influence the state of knowledge of learning and the means that one has to check this impact. We propose here our results of multi-field research carried out around the specification and the modeling of teaching activities in the field of the reading with an aim of building an environment of follow-up of learning. We present the methodology of work practised in the team, the structure of the specifications of activities, the computational models deduced and finally their implementation in an environment from follow-up from learning.Dans cet article, nous constatons qu'une activité ne peut pas être définie uniquement par sa forme effective et les connaissances du domaine visé qui sont mises en jeu. Nous proposons de définir une activité par les connaissances du domaine d'apprentissage mises en jeu, par sa forme mais également en ce qu'elle peut influer l'état de connaissance de l'apprenant et les moyens que l'on a de vérifier cet impact. Nous proposons ici nos résultats de recherche pluridisciplinaire menés autour de la spécification et la modélisation d'activités pédagogiques dans le domaine de la lecture dans le but de construire un environnement de suivi de l'apprenant. Nous présentons la méthodologie de travail pratiquée dans l'équipe, la structure des spécifications d'activités, les modèles computationnels déduits et finalement leur mise en œuvre dans un environnement de suivi de l'apprenant

Automatic Speech Emotion Recognition Using Machine Learning

This chapter presents a comparative study of speech emotion recognition (SER) systems. Theoretical definition, categorization of affective state and the modalities of emotion expression are presented. To achieve this study, an SER system, based on different classifiers and different methods for features extraction, is developed. Mel-frequency cepstrum coefficients (MFCC) and modulation spectral (MS) features are extracted from the speech signals and used to train different classifiers. Feature selection (FS) was applied in order to seek for the most relevant feature subset. Several machine learning paradigms were used for the emotion classification task. A recurrent neural network (RNN) classifier is used first to classify seven emotions. Their performances are compared later to multivariate linear regression (MLR) and support vector machines (SVM) techniques, which are widely used in the field of emotion recognition for spoken audio signals. Berlin and Spanish databases are used as the experimental data set. This study shows that for Berlin database all classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection are applied to the features. For Spanish database, the best accuracy (94 %) is achieved by RNN classifier without SN and with FS