Abstract
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.
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Vidrascu, L., Devillers, L. (2005). Real-Life Emotion Representation and Detection in Call Centers Data. In: Tao, J., Tan, T., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2005. Lecture Notes in Computer Science, vol 3784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_95
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DOI: https://doi.org/10.1007/11573548_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29621-8
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