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 i...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on audio, speech, and language processing speech, and language processing, 2011-07, Vol.19 (5), p.1057-1070
Main Authors: Mower, E, Matarić, Maja J, Narayanan, S
Format: Article
Language:English
Subjects:
Accuracy
Applied sciences
Classification
Design engineering
Emotion profiles
Emotions
EPS
Exact sciences and technology
Eyebrows
Feature extraction
Hidden Markov models
Human
Humans
Information, signal and communications theory
Labels
multimodal emotion classification
nonprototypical emotions
Recognition
Semantics
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Support vector machines
Telecommunications and information theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1558-7916
2329-9290
1558-7924
2329-9304
DOI:10.1109/TASL.2010.2076804