A Review and Meta-Analysis of Multimodal Affect Detection Systems

By S. D’Mello, Jacqueline Kory Westlund. ACM Computing Surveys, 2015

615 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/0dd09aeb7a9b8c4a43ec1dbc2ee3d4c445211bfc

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Citations in the corpus

AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition (2019, background): “works in the affective computing literature which supported this claim [9, 15].”
Multimodal Machine Learning: A Survey and Taxonomy (2017, background): “plest example of a joint representation is a concatenation of individual modality features (also referred to as early fusion [50]).”
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection (2018, methodology): “The motivation of this setup derives from previous works that demonstrate the effectiveness of multimodal features in creating rich feature representations (D’mello and Kory, 2015).”
Current State of Text Sentiment Analysis from Opinion to Emotion Mining (2017, methodology): “For further information regarding other types of sentiment analysis, one can refer to Yang and Chen [2012], El Ayadi et al. [2011], Zeng et al. [2009], Kleinsmith and Bianchi-Berthouze [2013], and D’mello and Kory [2015].”
Pedagogical Agents: Back to the Future (2018, positive): “Affect recognition is the ability to recognize a user’s affective state from a stream of input signals that may include facial expression, heart rate, electrodermal activity, and speech (Calvo and D’Mello 2010; D’Mello and Kory 2015).”
Identifying Stable Patterns over Time for Emotion Recognition from EEG (2016, background): “Although much progress has been made in the theories, methods and experiments that support affective computing over the past several years [4], the problem of detecting and modeling human emotions in aBCIs remains largely unexplored [3].”
EmotionMeter: A Multimodal Framework for Recognizing Human Emotions (2019, neutral): “robust emotion recognition models [7], [8].”
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos (2018, background): “An in-depth review of the literature in these systems is provided by D’mello and Kory, 2015.”