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Emotion expression is a complex process involving dependencies based on time, speaker, context, mood, personality, and culture. Emotion classification algorithms designed for real-world application must be able to interpret the emotional content of an utterance or dialog given the modulations resulting from these and other dependencies. Algorithmic development often rests on the assumption that the input emotions are uniformly recognized by a pool of evaluators. However, this style of consistent prototypical emotion expression often does not exist outside of a laboratory environment. This paper presents methods for interpreting the emotional content of non-prototypical utterances. These methods include modeling across multiple time-scales and modeling interaction dynamics between interlocutors. This paper recommends classifying emotions based on emotional profiles, or soft-labels, of emotion expression rather than relying on just raw acoustic features or categorical hard labels. Emotion expression is both interactive and dynamic. Consequently, to accurately recognize emotional content, these aspects must be incorporated during algorithmic design to improve classification performance.

Emotion is expressed and perceived through multiple modalities. In this work, we model face, voice and head movement cues for emotion recognition and we fuse classi ers using a Bayesian framework. The facial classi er is the best performing followed by the voice and head classi ers and the multiple modalities seem to carry complementary information, especially for happiness. Decision fusion signi cantly increases the average total unweighted accuracy, from 55% to about 62%. Overall, we achieve average accuracy on the order of 65-75% for emotional states and 30-40% for neutral state using a large multi-speaker, multimodal database. Performance analysis for the case of anger and neutrality suggests a positive correlation between the number of classi ers that performed well and the perceptual salience of the expressed emotion.