In daily conversations, people often encounter problems prompting conversational repair to enhance mutual understanding. By employing an automatic coreference solver, alongside examining repetition, we identify various linguistic features that distinguish turns when the addressee initiates repair from those when they do not. Our findings reveal distinct patterns that characterize the repair sequence and each type of repair initiation.

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.