Negative emotions detection as an indicator of dialogs quality in call centers

Negative emotions such as anger recognition in particular can deliver useful information to both the customer and the agent of Interactive Voice Response platforms. The state-of-the-art of emotion detection is characterized as not taking into account real-life emotion behavior but “realistic” induced emotion. This study is part of the French project Voxfactory (Cap Digital). The aim is to analyze the quality of the interactions collected in call centers by using the topics of the dialogs, but also informations on opinions and emotions. A corpus of 18 hours of real dialogs between agent and customer collected in a service of complaints of French company EDF (power supply) has been annotated with emotional labels. We describe experiments on detection of three emotional states during calls. Full speaker independent test set has been used in order to be closer to a real life situation. The novelty of this paper is the analysis of full conversations (including turns with low confidence in emotion annotation and noisy turns) and the impact on the detection score. The idea is to see how far we are from a system adapted to a real life situation.