Emotion recognition in human-computer interaction

By Nickolaos F. Fragopanagos, John G. Taylor. Neural Networks, 2005

1,847 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/1925241d58f08524d076cfd9e6506454b2e0475e

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Citations in the corpus

Speech Emotion Recognition Using Fourier Parameters (2015, methodology): “According to an extensive study by Cowie et al. [11], the acoustic correlations with voice quality can be grouped into voice level, pitch, phrase and feature boundaries and temporal structures.”
Emotion recognition based on physiological changes in music listening (2008, background): “Second, recording of biosignals requires the user to be bodily connected with biosensors and sensing using surface electrodes is very sensitive to motion artifacts.”
The New Handbook of Methods in Nonverbal Behavior Research (2008, background): “On the other hand, it may also be important to understand milder aVective states in speech such as moods and aVective stances (especially for some computer applications; Cowie et al. 2001), in which case regular samples of natural vocal expression are useful.”
A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition (2019, background): “As the key step to make machines capture human emotions, emotion recognition has received substantial attention from human–machine interaction (HMI) and pattern recognition communities in recent years [3]–[5].”
Affective e-Learning: Using "Emotional" Data to Improve Learning in Pervasive Learning Environment (2009, background): “Many efforts have been taken to recognize emotions using facial expressions, speech and physiological signals (Cowie et al., 2001; Healey, 2000; Picard et al., 2001).”
Survey on speech emotion recognition: Features, classification schemes, and databases (2011, background): “It is believed that the emotional content of an utterance is strongly related to its voice quality [29,109,31].”
Analysis of emotion recognition using facial expressions, speech and multimodal information (2004, background): “A comprehensive review of these approaches can be found in [6] and [19].”
3D Facial Expression Recognition Based on Primitive Surface Feature Distribution (2006, background): “The excellent review of recent advances in this field can be found in [5, 12, 17, 32].”
Primitives-based evaluation and estimation of emotions in speech (2007, neutral): “Most other studies involve 2 evaluators (Vidrascu and Devillers, 2005; Vidrascu and Devillers, 2005; Schuller et al., 2005) or, at most, 4–5 evaluators (Yu et al., 2004; Fragopanagos and Taylor, 2005; Lee and Narayanan, 2003).”
Emotion Recognition through Multiple Modalities: Face, Body Gesture, Speech (2008, background): “In this case, recognising users’ emotional state is one of the main requirements for computers to successfully interact with humans [2].”
ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors (2018, methodology): “modalities employed for AR include audio [14], [15], [16], visual [17], [18], [19] and audio-visual [20], [21], [22].”
Paralinguistics in speech and language - State-of-the-art and the challenge (2013, background): “Many scenarios based in particular on emotion recognition are further discussed in Cowie et al. (2001), icard (2003), and Batliner et al. (2006).”
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010, background): “Zeng et al. [12] reviewed the literature on audiovisual recognition approaches focusing on spontaneous expressions, while others [10], [13] focused on reviewing work on posed (e.g., by actors) emotions.”