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.

Comparative study of human age estimation based on hand-crafted and deep face features

In the past few years, human facial age estimation has drawn a lot of attention in the computer vision and pattern recognition communities because of its important applications in age-based image retrieval, security control and surveillance, biomet- rics, human-computer interaction (HCI) and social robotics. In connection with these investigations, estimating the age of a person from the numerical analysis of his/her face image is a relatively new topic. Also, in problems such as Image Classification the Deep Neural Networks have given the best results in some areas including age estimation. In this work we use three hand-crafted features as well as five deep features that can be obtained from pre-trained deep convolutional neural networks. We do a comparative study of the obtained age estimation results with these features.

A framework for abstraction and virtualization of sensors in mobile context-aware computing

110 p.

User Interface Abstraction for enabling TV set based Inclusive Access to the Information Society

199 p.

Desarrollo de una aplicación usando LEAP

[ES]Hoy en día existen diferentes alternativas para interactuar con los ordenadores. Sin embargo, las más extendidas y utilizadas son el teclado y el ratón. En ambos casos resulta necesario que las manos del usuario entren en contacto con algún dispositivo, ya sea un teclado físico o un ratón. En determinadas circunstancias en las que la higiene de las manos es un factor importante, este hecho puede suponer un inconveniente. En este proyecto de fin de grado se ha desarrollado KVLeap, una aplicación de escritorio para los sistemas Windows, que usando el controlador Leap Motion, un dispositivo que detecta y rastrea la posición y los movimientos de las manos en el aire, permite interactuar con un ordenador sin que las manos del usuario tengan que entrar en contacto con ningún dispositivo.