Real-Life Emotion Representation and Detection in Call Centers Data

Since the early studies of human behavior, emotions have attracted the interest of researchers in Neuroscience and Psychology. Recently, it has been a growing field of research in computer science. We are exploring how to represent and automatically detect a subject's emotional state. In contrast with most previous studies conducted on artificial data, this paper addresses some of the challenges faced when studying real-life non-basic emotions. Real-life spoken dialogs from call-center services have revealed the presence of many blended emotions. A soft emotion vector is used to represent emotion mixtures. This representation enables to obtain a much more reliable annotation and to select the part of the corpus without conflictual blended emotions for training models. A correct detection rate of about 80% is obtained between Negative and Neutral emotions and between Fear and Neutral emotions using paralinguistic cues on a corpus of 20 hours of recording.