  Automatic prediction of continuous-level emotional state requires selection
of suitable affective features to develop a regression system based on
supervised machine learning. This paper investigates the performance of
features statistically learned using convolutional neural networks for
instantaneously predicting the continuous dimensions of emotional states.
Features with minimum redundancy and maximum relevancy are chosen by using the
mutual information-based selection process. The performance of frame-by-frame
prediction of emotional state using the moderate length features as proposed in
this paper is evaluated on spontaneous and naturalistic human-human
conversation of RECOLA database. Experimental results show that the proposed
model can be used for instantaneous prediction of emotional state with an
accuracy higher than traditional audio or video features that are used for
affective computation.
