Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

Using the system dynamics paradigm in teaching and learning technological university subjets

2nd International Conference on Education and New Learning Technologies

Doubts and Equilibria

In real life strategic interactions, decision-makers are likely to entertain doubts about the degree of optimality of their play. To capture this feature of real choice-making, we present here a model based on the doubts felt by an agent about how well is playing a game. The doubts are coupled with (and mutually reinforced by) imperfect discrimination capacity, which we model here by means of similarity relations. We assume that each agent builds procedural preferences de ned on the space of expected payoffs-strategy frequencies attached to his current strategy. These preferences, together with an adaptive learning process lead to doubt-based selection dynamic systems. We introduce the concepts of Mixed Strategy Doubt Equilibria, Mixed Strategy Doubt-Full Equilibria and Mixed Strategy Doubtless Equilibria and show the theoretical and the empirical relevance of these concepts.

Metodología para la implantación de una herramienta de control virtual de robots serie

[Es]El objetivo principal de este trabajo es la introducción del usuario al mundo de la robótica, explicando para ello, desde un punto de vista práctico, los conceptos teóricos relacionados con la cinemática de mecanismos espaciales, específicamente la de los robots serie. Para lograr este objetivo se ha creado una metodología de aprendizaje, basada en tres ejercicios, que explica los comandos principales de RobotStudio; software de programación necesario para el control virtual de robots de la marca ABB, robot disponible en la escuela. Junto con esto, se desarrollan los conceptos necesarios para la realización de tareas básicas dentro del ámbito de la robótica. Mediante la implantación de esta metodología se pretende dotar al usuario de los conceptos esenciales para programar robots serie dentro de un ámbito virtual, otorgándole la posibilidad de conectarlo posteriormente a un robot real, obteniendo resultados prácticos y visibles.