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