Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
IEEE Transactions on Pattern Analysis and Machine Intelligence
AVEC 2011-the first international audio/visual emotion challenge
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Elastic net for paralinguistic speech recognition
Proceedings of the 14th ACM international conference on Multimodal interaction
LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework
Image and Vision Computing
Shape-based modeling of the fundamental frequency contour for emotion detection in speech
Computer Speech and Language
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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.