  As speech-interfaces are getting richer and widespread, speech emotion
recognition promises more attractive applications. In the continuous emotion
recognition (CER) problem, tracking changes across affective states is an
important and desired capability. Although CER studies widely use correlation
metrics in evaluations, these metrics do not always capture all the
high-intensity changes in the affective domain. In this paper, we define a
novel affective burst detection problem to accurately capture high-intensity
changes of the affective attributes. For this problem, we formulate a two-class
classification approach to isolate affective burst regions over the affective
state contour. The proposed classifier is a kernel-fusion dilated convolutional
neural network (KFDCNN) architecture driven by speech spectral features to
segment the affective attribute contour into idle and burst sections.
Experimental evaluations are performed on the RECOLA and CreativeIT datasets.
The proposed KFDCNN is observed to outperform baseline feedforward neural
networks on both datasets.
