Affective computing: challenges

By Rosalind W. Picard. Int. J. Hum. Comput. Stud., 2003

924 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/376cf14b5bf4d0e636233d4344757e5e44cda285

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In Experimental and Cognitive Psychology

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Citations in the corpus

Paralinguistics in speech and language - State-of-the-art and the challenge (2013, background): “Information about speaker state r traits can be exploited even in the acoustic layer to improve recognition of ‘what’ has been said, e.g., by acoustic odel adaptation (Fernandez and Picard, 2003; Athanaselis et al., 2005; Steidl et al., 2010).”
Human Emotion Recognition: Review of Sensors and Methods (2020, background): “Machinery for recognizing, expressing, modelling, communicating, and responding to information about emotions as well as certain affective computing instances have been built globally by numerous researchers [197].”
Fundamentals of physiological computing (2009, background): “…(2) the enormous range of psychological states cannot be represented by physiology, (3) the lack of clear differentiation between psychological states, and (4) the variable and idiosyncratic experience of psychological states (see Picard (2003) for further detail and discussion of this point).”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, background): “Picard stated that emotions play a vital role in rational decision-making, learning, and perception, as well as in a variety of functions (Picard, 2003).”
Affective Computing: A Review (2005, background): “This question has been discussed by Picard in her paper [ 63 ].”
Text‐based emotion detection: Advances, challenges, and opportunities (2020, background): “Hence, they compared the performance of three (3) word embeddings viz, the Google Word Embedding, GloVe Embedding, and the FastText”
A review of verbal and non-verbal human-robot interactive communication (2014, background): “The affective dimension is very important in human interaction [98], because it is strongly intertwined with learning [99], persuasion [100], and empathy, among many other functions.”
A Survey of Augmented Reality Technologies, Applications and Limitations (2010, background): “Conversely, a laser range finder used for environmental modelling may also enable users themselves to place notes into the environment [123].”
Collaborative Virtual Environment Technology for People With Autism (2005, background): “Similarly, George and McIlhagga (2000) pointed to open questions concerning the design of affective behavior of virtual characters for children, and there is much recent interest in the field of “affective computing” in general (see, e.g., Hudlicka, 2003; Picard, 2003); our results may contribute to”
Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks (2015, background): “Although affective computing has achieved rapid development in recent years, there are still many open problems to be solved [5], [6].”
A 3D facial expression database for facial behavior research (2006, background): “Research on automatic techniques for analyzing human facial behavior has been conducted for over three decades [11, 32, 34].”
Value and reward based learning in neurorobots (2013, background): “Picard, R. W. (2003). Affective computing: challenges. Int. J. Hum. Comput. Stud. 59, 55. Scherer, K. R., Banse, R., Wallbott, H. G., and Goldbeck, T. (1991). Vocal cues in emotion encoding and decoding.”