Analysis of Anger across several agent-customer interactions in French call centers

In this article, the detection of real-life emotions is explored across different corpora featuring a similar task. Two emotional states (Anger and Neutral) are thus examined across three French corpora collected in call centers in different contexts (service complaints, Stock Exchange service and medical emergency). The effects of these differents contexts on the performance of acoustic-based emotion detection systems are investigated with two objectives in mind: improving the generalization power of existing models by handling these differences and trying to quantify the differences between corpora in order to easily compare them. The described experiments are based on the following approach: examining the detection performance when training on a corpus and testing on another one or when training on mixtures of the corpora.