Abstract

Human emotional expression tends to evolve in a structured manner in the sense that certain emotional evolution patterns, i.e., anger to anger, are more probable than others, e.g., anger to happiness. Furthermore, the perception of an emotional display can be affected by recent emotional displays. Therefore, the emotional content of past and future observations could offer relevant temporal context when classifying the emotional content of an observation. In this work, we focus on audio-visual recognition of the emotional content of improvised emotional interactions at the utterance level. We examine context-sensitive schemes for emotion recognition within a multimodal, hierarchical approach: bidirectional Long Short-Term Memory (BLSTM) neural networks, hierarchical Hidden Markov Model classifiers (HMMs), and hybrid HMM/BLSTM classifiers are considered for modeling emotion evolution within an utterance …

Date

2012

Authors

Angeliki Metallinou, Martin Wollmer, Athanasios Katsamanis, Florian Eyben, Bjorn Schuller, Shrikanth Narayanan

Journal

IEEE Transactions on Affective Computing

Volume

3

Issue

2

Pages

184-198

Publisher

IEEE