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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Investigating the Use of Formant Based Features for Detection of Affective Dimensions in Speech
verfasst von : Jonathan C. Kim, Hrishikesh Rao, Mark A. Clements
Erschienen in: Affective Computing and Intelligent Interaction
Verlag: Springer Berlin Heidelberg
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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.