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Artificial intelligence and machine learning systems have demonstrated huge improvements and human-level parity in a range of activities, including speech recognition, face recognition and speaker verification.
The priori emotion dataset: Linking mood to emotion detected in-the-wild
Khorram, S., Jaiswal, M., Gideon, J., McInnis, M., and Provost, E.-M. (2018) · 1907
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A scale for the judgment of facial expressions
Schlosberg, H. (1941) · 1941
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Three dimensions of emotion
Schlosberg, H. (1954) · 1954
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An approach to environmental psychology
Mehrabian, A. and Russell, J. A. (1974) · 1974
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Emotion: A Psychoevolutionary Synthesis
Plutchik, R. (1980) · 1980
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A circumplex model of affect
Russell, J. A. (1980) · 1980
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On the nature and function of emotion: A component process approach
Scherer, K. (1984) · 1984
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Toward a consensual structure of mood
Watson, D. and Tellegen, A. (1985) · 1985
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The emotions
Frijda, N. H. (1986) · 1986
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The cognitive structure of emotions
Ortony, A., Clore, G. L., and Collins, A. (1990) · 1990
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Forced-choice response format in the study of facial expression
Russell, J. A. (1993) · 1993
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Affective wearables
Picard, R. W. and Healey, J. (1997) · 1997
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’FEELTRACE’: An instrument for recording perceived emotion in real time
Cowie, R., Douglas-Cowie, E., Savvidou, S., McMahon, E., Sawey, M., and Schröder, M. (2000) · 2000
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Affective computing
Picard, R. W. (2000) · 2000
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Emotion recognition in human-computer interaction
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., and Taylor, J. G. (2001) · 2001
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Acoustic correlates of emotion dimensions in view of speech synthesis
Schröder, M., Cowie, R., Douglas-Cowie, E., Westerdijk, M., and Gielen, S. (2001) · 2001
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Describing the emotional states that are expressed in speech
Cowie, R. and Cornelius, R. (2003) · 2003
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Vocal communication of emotion: A review of research paradigms
Scherer, K. (2003) · 2003
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Learning to rank using gradient descent
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., and Hullender, G. (2005) · 2005
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Challenges in real-life emotion annotation and machine learning based detection
Devillers, L., Vidrascu, L., and Lamel, L. (2005) · 2005
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Evaluation of natural emotions using self assessment manikins
Grimm, M. and Kroschel, K. (2005) · 2005
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Real-life emotion representation and detection in call centers data
Vidrascu, L. and Devillers, L. (2005) · 2005
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Training linear SVMs in linear time
Joachims, T. (2006) · 2006
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Practical Approaches to Comforting Users with Relational Agents
Bickmore, T. and Schulman, D. (2007) · 2007
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Primitives-based evaluation and estimation of emotions in speech
Grimm, M., Kroschel, K., Mower, E., and Narayanan, S. (2007) · 2007
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Multimodal human-computer interaction: A survey
Jaimes, A. and Sebe, N. (2007) · 2007
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What should a generic emotion markup language be able to represent?
Schröder, M., Devillers, L., Karpouzis, K., Martin, J.-C., Pelachaud, C., Peter, C., Pirker, H., Schuller, B., Tao, J., and Wilson, I. (2007) · 2007
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Releasing a thoroughly annotated and processed spontaneous emotional database: the FAU Aibo emotion corpus
Batliner, A., Steidl, S., and Nöth, E. (2008) · 2008
Cited alongside, same era.
Evaluating evaluators: A case study in understanding the benefits and pitfalls of multi-evaluator modeling
Mower, E., Matarić, M. J., and Narayanan, S. S. (2009) · 2009
Cited alongside, same era.
Relational Agents in Clinical Psychiatry
Bickmore, T. and Gruber, A. (2010) · 2010
Cited alongside, same era.
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A. and D’Mello, S. (2010) · 2010
Cited alongside, same era.
Quantification of prosodic entrainment in affective spontaneous spoken interactions of married couples
Lee, C.-C., Black, M., Katsamanis, A., Lammert, A. C., Baucom, B. R., Christensen, A., Georgiou, P. G., and Narayanan, S. S. (2010) · 2010
Cited alongside, same era.
Needs and challenges in human computer interaction for processing social emotional information
Esposito, A., Esposito, A. M., and Vogel, C. (2015) · 2015
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Correcting time-continuous emotional labels by modeling the reaction lag of evaluators
Mariooryad, S. and Busso, C. (2015) · 2015
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Emotion markup language
Schröder, M., Baggia, P., Burkhardt, F., Pelachaud, C., Peter, C., and Zovato, E. (2015) · 2015
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Open Challenges in Modelling, Analysis and Synthesis of Human Behaviour in Human–Human and Human–Machine Interactions
Vinciarelli, A., Esposito, A., André, E., Bonin, F., Chetouani, M., Cohn, J. F., Cristani, M., Fuhrmann, F., Gilmartin, E., Hammal, Z., Heylen, D., Kaiser, R., Koutsombogera, M., Potamianos, A., Renals, S., Riccardi, G., and Salah, A. A. (2015) · 2015
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Modeling the affective content of music with a gaussian mixture model
Wang, J.-C., Yang, Y.-H., Wang, H.-M., and Jeng, S.-K. (2015) · 2015
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Classification of complex information: Inference of co-occurring affective states from their expressions in speech
Sobol-Shikler, T. and Robinson, P. (2010) · 2010
Cited alongside, same era.
Emotion representation, analysis and synthesis in continuous space: A survey
Gunes, H., Schuller, B., Pantic, M., and Cowie, R. (2011) · 2011
Cited alongside, same era.
Aesthetics and emotions in images
Joshi, D., Datta, R., Fedorovskaya, E., Luong, Q.-T., Wang, J. Z., Li, J., and Luo, J. (2011) · 2011
Cited alongside, same era.
A framework for automatic human emotion classification using emotion profiles
Mower, E., Mataric, M. J., and Narayanan, S. (2011) · 2011
Cited alongside, same era.
Detecting naturalistic expressions of nonbasic affect using physiological signals
AlZoubi, O., D’Mello, S. K., and Calvo, R. A. (2012) · 2012
Cited alongside, same era.
Frontiers of Affect-Aware Learning Technologies
Calvo, R. A. and D’Mello, S. (2012) · 2012
Cited alongside, same era.
The SEMAINE database: Annotated multimodal records of emotionally colored conversations between a person and a limited agent
McKeown, G., Valstar, M., Cowie, R., Pantic, M., and Schröder, M. (2012) · 2012
Cited alongside, same era.
Emotion distribution recognition from facial expressions
Zhou, Y., Xue, H., and Geng, X. (2015) · 2015
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Modeling subjectiveness in emotion recognition with deep neural networks: Ensembles vs soft labels
Fayek, H. M., Lech, M., and Cavedon, L. (2016) · 2016
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Using agreement on direction of change to build rank-based emotion classifiers
Parthasarathy, S., Cowie, R., and Busso, C. (2016) · 2016
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Gaussian process regression for continuous emotion recognition with global temporal invariance
Atcheson, M., Sethu, V., and Epps, J. (2017) · 2017
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MSP-IMPROV: An acted corpus of dyadic interactions to study emotion perception
Busso, C., Parthasarathy, S., Burmania, A., AbdelWahab, M., Sadoughi, N., and Mower Provost, E. (2017) · 2017
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An investigation of emotion prediction uncertainty using gaussian mixture regression
Dang, T., Sethu, V., Epps, J., and Ambikairajah, E. (2017) · 2017
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From hard to soft: Towards more human-like emotion recognition by modelling the perception uncertainty
Han, J., Zhang, Z., Schmitt, M., Pantic, M., and Schuller, B. (2017) · 2017
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Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features
Harley, J. M., Lajoie, S. P., Frasson, C., and Hall, N. C. (2017) · 2017
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Formulating emotion perception as a probabilistic model with application to categorical emotion classification
Lotfian, R. and Busso, C. (2017) · 2017
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Automatic assessment of depression based on visual cues: A systematic review
Pampouchidou, A., Simos, P., Marias, K., Meriaudeau, F., Yang, F., Pediaditis, M., and Tsiknakis, M. (2017) · 2017
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A survey on mobile affective computing
Politou, E., Alepis, E., and Patsakis, C. (2017) · 2017
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The ordinal nature of emotions
Yannakakis, G., Cowie, R., and Busso, C. (2017) · 2017
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Predicting the distribution of emotion perception: capturing inter-rater variability
Zhang, B., Essl, G., and Mower Provost, E. (2017) · 2017
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Demonstrating and modelling systematic time-varying annotator disagreement in continuous emotion annotation
Atcheson, M., Sethu, V., and Epps, J. (2018) · 2018
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Dynamic multi-rater gaussian mixture regression incorporating temporal dependencies of emotion uncertainty using kalman filters
Dang, T., Sethu, V., and Ambikairajah, E. (2018) · 2018
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Human-like emotion recognition: Multi-label learning from noisy labeled audio-visual expressive speech
Kim, Y. and Kim, J. (2018) · 2018
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Predicting categorical emotions by jointly learning primary and secondary emotions through multitask learning
Lotfian, R. and Busso, C. (2018) · 2018
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Jointly aligning and predicting continuous emotion annotations
Khorram, S., McInnis, M., and Provost, E.-M. (2019) · 2019
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Building naturalistic emotionally balanced speech corpus by retrieving emotional speech from existing podcast recordings
Lotfian, R. and Busso, C. (2019) · 2019
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Into the wild: Transitioning from recognizing mood in clinical interactions to personal conversations for individuals with bipolar disorder
Matton, K., McInnis, M. G., and Mower Provost, E. (2019) · 2019
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The ordinal nature of emotions: An emerging approach
Yannakakis, G., Cowie, R., and Busso, C. (2019) · 2019
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