Adaptive curiosity for emotions detection in speech

Exploratory activities seem to be crucial for our cognitive development. According to psychologists, exploration is an intrinsically rewarding behaviour. The developmental robotics aims to design computational systems that are endowed with such an intrinsic motivation mechanism. There are possible links between developmental robotics and machine learning. Affective computing takes into account emotions in human machine interactions for intelligent system design. The main difficulty to implement automatic detection of emotions in speech is the prohibitive labelling cost of data. Active learning tries to select the most informative examples to build a training set for a predictive model. In this article, the adaptive curiosity framework is used in terms of active learning terminology, and directly compared with existing algorithms on an emotion detection problem.