In this paper we describe a corpus set together from two sub-corpora. The CINEMO corpus contains acted emotional expression obtained by playing dubbing exercises. This new protocol is a way to collect mood-induced data in large amount which show several complex and shaded emotions. JEMO is a corpus collected with an emotion-detection game and contains more prototypical emotions than CINEMO. We show how the two sub-corpora balance and enrich each other and result in a better performance. We built male and female emotion models and use Sequential Fast Forward Feature Selection to improve detection performances. After feature-selection we obtain good results even with our strict speaker independent testing method. The global corpus contains 88 speakers (38 females, 50 males). This study has been done within the scope of the ANR (National Research Agency) Affective Avatar project which deals with building a system of emotions detection for monitoring an Artificial Agent by voice.

The CINEMO corpus of French emotional speech provides a richly annotated resource to help overcome the apparent lack of learning and testing speech material for complex, i.e. blended or mixed emotions. The protocol for its collection was dubbing selected emotional scenes from French movies. 51 speakers are contained and the total speech time amounts to 2 hours and 13 minutes and 4k speech chunks after segmentation. Extensive labelling was carried out in 16 categories for major and minor emotions and in 6 continuous dimensions. In this contribution we give insight into the corpus statistics focusing in particular on the topic of complex emotions, and provide benchmark recognition results obtained in exemplary large feature space evaluations. In the result the labelling oft he collected speech clearly demonstrates that a complex handling of emotion seems needed. Further, the automatic recognition experiments provide evidence that the automatic recognition of blended emotions appears to be feasible.