Aitor Álvarez

TL;DR: A new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition by means of an integration of an estimation of distribution algorithm (EDA) in the first layer to select the optimal subset from the standard base classifiers.

TL;DR: A study performed to analyze different Machine Learning techniques validity in automatic speech emotion recognition area using a bilingual affective database and techniques based on evolutive algorithms to select speech feature subsets that optimize automatic emotion recognition success rate.

TL;DR: An attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection shows 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.

TL;DR: In this article, different speech parameters have been calculated for each audio recording and several Machine Learning techniques have been applied to evaluate their usefulness in speech emotion recognition in this particular case, techniques based on evolutive algorithms have been used to select speech feature subsets that optimize automatic emotion recognition success rate.

TL;DR: This paper describes the data collection, annotation and sharing activities carried out within the FP7 EU-funded SAVAS project, which aims to collect, share and reuse audiovisual language resources from broadcasters and subtitling companies to develop large vocabulary continuous speech recognisers in specific domains and new languages.