Abstract

The goal of emotion classification is to estimate an emotion label, given representative data and discriminative features. Humans are very good at deriving high-level representations of emotion state and integrating this information over time to arrive at a final judgment. However, currently, most emotion classification algorithms do not use this technique. This paper presents a hierarchical static dynamic emotion classification framework that estimates high-level emotional judgments and locally integrates this information over time to arrive at a final estimate of the affective label. The results suggest that this framework for emotion classification leads to more accurate results than either purely static or purely dynamic strategies.

Date

May 22, 2011

Authors

Emily Mower, Shrikanth Narayanan

Conference

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

2372-2375

Publisher

IEEE