This paper presents the results of perceptual and statistical investigation of four emotions: anger, happiness, fear and sadness, in comparison to neutral speech. Perceptual analysis was performed through two tests: emotion evaluation inside Plutchik's circle and emotion recognition test with subsequent statistical analysis with MDS (multidimensional scaling) procedure. Statistical analysis of emotions was based on static and dynamic acoustic features extracted from speech signals. ANOVA analysis of each class of features has given distribution of features according to its importance for emotion discrimination. Correlation analysis of each dimension of three-dimensional MDS representation with selected features indicate that ones of most importance in emotion identification. Finally, a three-level hierarchical model of emotion recognition was proposed.
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