Abstract
The ability of a machine to discern various categories of emotion is of great interest in many applications. This paper attempts to explore the use of baseline features consisting of prosodic and spectral features along with formant based features for the purpose of classification of emotion along the dimensions of arousal, valence, expectancy, and power. Using three feature selection criteria namely maximum average recall, maximal relevance, and minimal-redundancy-maximal-relevance, the paper intends to find the criterion that gives the highest unweighted accuracy. Using a Gaussian Mixture Model classifier, the results indicate that the formant based features show a statistically significant improvement on the accuracy of the classification system.
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Kim, J.C., Rao, H., Clements, M.A. (2011). Investigating the Use of Formant Based Features for Detection of Affective Dimensions in Speech. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_48
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DOI: https://doi.org/10.1007/978-3-642-24571-8_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24570-1
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