Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Several prior works show that combining multiple cues (multimodal) results in higher accuracy in automatic emotion recognition.
“Moreover, several prior works show that combining multiple cues (multimodal) can results in higher accuracy in automatic emotion recognition [4], [5].”
From EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge (2023) · cites this paper as [4]
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References 125 · Cited by 323
- Authors
- D’Mello, S. K., & Graesser, A.
- Affiliations
- University of Memphis (US)
- Corresponding author
- D’Mello, Sidney K.
- Venue
- User Modeling and User-Adapted Interaction
- Published
- 2010-05-04
- Volume
- 20
- Issue
- 2
- Pages
- 147-187
- ISSN
- 0924-1868 (ISSN-L)
- 1573-1391
- OpenAlex
- W1995641269
- Cited by
- 323
- References
- 125
- Citations by year
- 2026: 18 · 2025: 17 · 2024: 15 · 2023: 23 · 2022: 12 · 2021: 12 · 2020: 17 · 2019: 27 · 2018: 25 · 2017: 20 · 2016: 20 · 2015: 22 · 2014: 20 · 2013: 21 · 2012: 23
- FWCI
- 31.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- User Modeling and User-Adapted Interaction (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- affect recognition, multimodal emotion recognition, conversational cues, body language, facial expressions, linear discriminant analysis, feature-level fusion, AutoTutor, intelligent tutoring systems, affective learning, boredom, engagement, frustration, confusion, delight
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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