Bayesian Inference Based Temporal Modeling for Naturalistic Affective Expression Classification

In real life, the affective state of human beings changes gradually and smoothly. There is a high probability that the affective state of a certain moment depends on the states of a previous period. In this study, we propose to explicitly model the temporal relationship using a Bayesian inference based two-stage classification approach. This approach could involve knowledge about the dynamics of affective states during a period, so that the inferred affective states are predicted by considering a certain amount of context. Evaluations on the Audio Sub-Challenge of the 2011 Audio/Visual Emotion Challenge show our approach obtains competitive results to those of Audio Sub-Challenge winners. The temporal context modeling method proposed in this paper is also helpful for other sequential pattern recognition problems.