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This paper presents a widespread analysis of affective vocal expressionclassification systems. In this study, state-of-the-art acoustic features arecompared to two novel affective vocal prints for the detection of emotionalstates: the Hilbert-Huang-Hurst Coefficients (HHHC) and the vector of index ofnon-stationarity (INS). HHHC is here proposed as a nonlinear vocal sourcefeature vector that represents the affective states according to their effectson the speech production mechanism. Emotional states are highlighted by theempirical mode decomposition (EMD) based method, which exploits thenon-stationarity of the affective acoustic variations. Hurst coefficients(closely related to the excitation source) are then estimated from thedecomposition process to compose the feature vector. Additionally, the INSvector is introduced as dynamic information to the HHHC feature. The proposedfeatures are evaluated in speech emotion classification experiments with threedatabases in German and English languages. Three state-of-the-art acousticfeatures are adopted as baseline. The $\alpha$-integrated Gaussian model($\alpha$-GMM) is also introduced for the emotion representation andclassification. Its performance is compared to competing stochastic and machinelearning classifiers. Results demonstrate that HHHC leads to significantclassification improvement when compared to the baseline acoustic features.Moreover, results also show that $\alpha$-GMM outperforms the competingclassification methods. Finally, HHHC and INS are also evaluated ascomplementary features for the GeMAPS and eGeMAPS feature sets

Objective Human Affective Vocal Expression Detection and Automatic Classification with Stochastic Models and Learning Systems