Literaturaren zertarakoa, itzulpenak eta hizkuntzaren auzia Orixerengan

Ejemplar dedicado a: Beñat Oihartzabali gorazarre - Festchrift for Bernard Oyharçabal/ Ricardo Etxepare, Ricardo Gómez, Joseba Andoni Lakarra ( eds.)

Augmented Peter Pan: un cuento multilingüe con Realidad Aumentada

[SPA] El objetivo de este Trabajo de Fin de Grado ha sido diseñar un cuento multilingüe que incorpora una innovación tecnológica, la Realidad Aumentada, para favorecer el proceso de enseñanza-aprendizaje lingüístico en la escuela. Para ello, hemos analizado algunos cuentos con Realidad Aumentada que se pueden encontrar en el mercado. Al no encontrar ningún cuento que cumpliese nuestras expectativas, hemos creado un cuento multilingüe con Realidad Aumentada adaptando el cuento clásico “Peter Pan”, que cumple los objetivos de fomentar el aprendizaje de diferentes idiomas y el gusto por la lectura, valorar las lenguas inmigrantes en las aulas y promover el uso de las TIC.

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.