An audiovisual dataset for emotion recognition (UTeMo) in Spanish language

Abstract

Emotion recognition has had remarkable growth in human-computer communication because emotions play an important role in the daily lives of human beings. This task is challenging because emotions are bodily reactions to external stimuli that differ greatly between people. Societies tend to normalize the response to these stimuli depending on their culture and era. In the literature, there are databases designed for the recognition of emotions in various languages, principally English. Although it is possible to find databases in Spanish within this compendium, none of them are specialized in the variant of Mexican Spanish, and most of them contain low image resolution and audio fidelity, which limit their utility for emotion recognition through deep learning as convolutional networks. For these reasons, this data article introduces UTeMo, a new dataset in Spanish language which includes the six basic emotions based on the Paul Ekman's model (anger, sadness, joy, surprise, fear, and disgust) for facial and voice expressions plus a neutral state. The proposed dataset is qualitatively validated to provide a feasible and reliable basis for emotion recognition using machine and deep learning methods.