Detection of real-life emoti

In contrast to most previous studies conducted on artificial data with archetypal emotions, this paper addresses some of the challenges faced when studying real-life non-basic emotions. A 10-hour dialog corpus recorded in a French Medical emergency call center has been studied. In this paper, our aim is twofold, to study emotion mixtures in natural data and to achieve high level of performances in real-life emotion detection. An annotation scheme using two labels per segment has been used for representing emotion mixtures. A closer study of these mixtures has been carried out, revealing the presence of conflictual valence emotions. A correct detection rate of about 82% was obtained between Negative and Positive emotions using paralinguistic cues without taking into account these conflictual blended emotions. A perceptive study and the confusions analysis obtained with automatic detection enable to validate the annotation scheme with two emotion labels per segment.