The emotion detection work reported here is part of a larger study aiming to model user behavior in real interactions. We already studied emotions in a real-life corpus with human-human dialogs on a financial task. We now make use of another corpus of real agent-caller spoken dialogs from a medical emergency call center in which emotion manifestations are much more complex, and extreme emotions are common. Our global aims are to define appropriate verbal labels for annotating real-life emotions, to annotate the dialogs, to validate the presence of emotions via perceptual tests and to find robust cues for emotion detection. Annotations have been done by two experts with twenty verbal classes organized in eight macro-classes. We retained for experiments in this paper four macro classes: Relief, Anger, Fear and Sadness. The relevant cues for detecting natural emotions are paralinguistic and linguistic. Two studies are reported in this paper: the first investigates automatic emotion detection using linguistic information, whereas the second investigates emotion detection with paralinguistic cues. On the medical corpus, preliminary experiments using lexical cues detect about 78% of the four labels showing very good detection for Relief (about 90%) and Fear (about 86%) emotions. Experiments using paralinguistic cues show about 60% of good detection, Fear being best detected.