A Framework for Automatic Human Emotion Classification Using Emotion Profiles

Automatic recognition of emotion is becoming an increasingly important component in the design process for affect-sensitive human-machine interaction (HMI) systems. Well-designed emotion recognition systems have the potential to augment HMI systems by providing additional user state details and by informing the design of emotionally relevant and emotionally targeted synthetic behavior. This paper describes an emotion classification paradigm, based on emotion profiles (EPs). This paradigm is an approach to interpret the emotional content of naturalistic human expression by providing multiple probabilistic class labels, rather than a single hard label. EPs provide an assessment of the emotion content of an utterance in terms of a set of simple categorical emotions: anger; happiness; neutrality; and sadness. This method can accurately capture the general emotional label (attaining an accuracy of 68.2% in our experiment on the IEMOCAP data) in addition to identifying underlying emotional properties of highly emotionally ambiguous utterances. This capability is beneficial when dealing with naturalistic human emotional expressions, which are often not well described by a single semantic label.