Appropriate emotional labelling of non-acted speech using basic emotions, geneva emotion wheel and self assessment manikins

Authors:
I. Siegert;R. Bock;B. Vlasenko;D. Philippou-Hubner;A. Wendemuth
Affiliations:
Otto von Guericke University Magdeburg, Dept. of Electrical Engineering and Information Technology, P.O. Box 4120, 39016 Magdeburg, Germany;Otto von Guericke University Magdeburg, Dept. of Electrical Engineering and Information Technology, P.O. Box 4120, 39016 Magdeburg, Germany;Otto von Guericke University Magdeburg, Dept. of Electrical Engineering and Information Technology, P.O. Box 4120, 39016 Magdeburg, Germany;Otto von Guericke University Magdeburg, Dept. of Electrical Engineering and Information Technology, P.O. Box 4120, 39016 Magdeburg, Germany;Otto von Guericke University Magdeburg, Dept. of Electrical Engineering and Information Technology, P.O. Box 4120, 39016 Magdeburg, Germany
Venue:
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
Year:
2011

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Computer Speech and Language

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Abstract

In emotion recognition from speech, a good transcription and annotation of given material is crucial. Moreover, the question of how to find good emotional labels for new data material is a basic issue. It is not only the question of which emotion labels to choose, it is also a matter of how labellers can cope with annotation methods. In this paper, we present our investigations for emotional labelling with three different methods (Basic Emotions, Geneva Emotion Wheel and Self Assessment Manikins) and compare them in terms of emotion coverage and usability. We show that emotion labels derived from Geneva Emotion Wheel or Self Assessment Manikins fulfill our requirements, but Basic Emotions are not feasible for emotion labelling from spontaneous speech.