Facing Reality: Simulating Deployment of Anger Recognition in IVR Systems

With the availability of real-life corpora studies dealing with speech-based emotion recognition have turned towards recognition of angry users on turn level. Based on acoustic, linguistic and sometimes contextual features classifiers yield performance values of 0.7-0.8 f-score when classifying angry vs. non-angry user turns. The effect of deploying anger classifiers in real systems still remains an open point and has not been examined so far. Is the current performance of anger detection already adequate enough for a change in dialogue strategy or even an escalation to an operator? In this study we explore the impact of an anger classifier that has been published in a previous study on specific dialogues. We introduce a cost-sensitive classifier that reduces the number of misclassified non-angry user turns significantly.