A Framework for Automatic Human Emotion Classification Using Emotion Profiles
Abstract
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.
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All features are normalized to avoid person dependency.
“All of these features are normalized to avoid person dependency [28].”
From Multimodal emotion recognition using deep learning architectures (2016) · cites this paper as [28]
Cites
Barrett, L. F. (2006) · Perspectives on Psychological Science · doi:10.1111/j.1745-6916.2006.00003.x · Cited by 1,859 · see citing works
The INTERSPEECH 2009 emotion challenge
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Analysis of emotion recognition using facial expressions, speech and multimodal information
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“Multimodal classification of emotion is widely used across the community [5], [18][19].”
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From Eliza to XiaoIce: Challenges and opportunities with social chatbots
Shum, H.-Y., He, X.-D., & Li, D. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700826 · Cited by 749 · see citing works
“It also detects the sentiment reflected in the input message and infers the user’s emotion status (Tokuhisa et al., 2008; Mower et al., 2011; Socher et al., 2013; Yang et al., 2016; Chen et al., 2016).”
BackgroundDeep learning for robust feature generation in audiovisual emotion recognition
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MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception
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Multimodal emotion recognition using deep learning architectures
Ranganathan, H., Chakraborty, S., & Panchanathan, S. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477679 · Cited by 249 · see citing works
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Automatic recognition of self-reported and perceived emotion: does joint modeling help?
Zhang, B., Essl, G., & Mower Provost, E. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2993173 · Cited by 21 · see citing works
(2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-72038-8 · Cited by 11 · see citing works
- Authors
- Mower, E., Mataric, M. J., & Narayanan, S.
- Affiliations
- University of Southern California (US)
- ORCID
- Narayanan, Shrikanth
- Venue
- IEEE Transactions on Audio, Speech, and Language Processing
- Published
- 2011-07
- Volume
- 19
- Issue
- 5
- Pages
- 1057-1070
- ISSN
- 1558-7916 (ISSN-L)
- 1558-7924
- OpenAlex
- W2154024118
- Cited by
- 215
- References
- 3
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 11 · 2023: 18 · 2022: 12 · 2021: 14 · 2020: 10 · 2019: 15 · 2018: 16 · 2017: 20 · 2016: 18 · 2015: 26 · 2014: 13 · 2013: 22 · 2012: 6
- FWCI
- 14.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Audio Speech and Language Processing (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- emotion recognition, emotion profiles, IEMOCAP, natural speech, emotional ambiguity, anger, happiness, sadness, neutrality
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