Detecting Anger in Automated

Anger detection is a topic that is gaining more and more attention with voice portal carriers, as it can be useful for quality measurement and emotion-aware dialog strategies. In the context of a prototype voice portal we describe methods to search for training data, report on the performance of the prosodic classifier under real world conditions and explore the use of dialog information for anger prediction. The results show that, although significantly worse than under laboratory conditions, anger detection still works well above chance level and can be used to enhance real world voice-portal usability.