MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Getting closer: tailored human–computer speech dialog
Universal Access in the Information Society
On NoMatchs, NoInputs and BargeIns: do non-acoustic features support anger detection?
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Advances in the Witchcraft workbench project
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Modeling and predicting quality in spoken human-computer interaction
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
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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.