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.

Environmental judgment in early childhood and its relationship with the understanding of the concept of living beings

The evidence collected concerning the biocentric judgment that young children express when evaluating human actions on the environment leads some scholars to suggest that an essential understanding of the notion of living beings should appear earlier than previously believed. This research project aims to study that assumption. To this end, young children’s choice when they are put in situation of having to compare and choose the most negative option between environmentally harmful actions and the breaking of social conventions are examined. Afterwards, the results are categorized in relation to those obtained from the study of children’s grasp of the distinction between living beings and inanimate entities. The data is analysed according to the individuals’ age and overall, it suggests a lack of relationship between environmental judgment and the understanding of the concept of living beings. The final results are discussed in keeping with recent research in the field of moral development that underscores the role that unconscious emotional processing plays in the individual’s normative judgment.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

Together we have it all ! Benefits of participation in collective emotional gatherings and communal coping

244 p.