The ordinal nature of emotions
By Georgios N. Yannakakis, R. Cowie, Carlos Busso. 2017
106 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Maja Pantic
- Michel François Valstar
- R. Cowie
- Björn Schuller
- Fabien Ringeval
- Mohammad Soleymani
- Kevin El Haddad
- Thierry Dutoit
In Computer Vision and Pattern Recognition
- Michel François Valstar
- Maja Pantic
- Björn Schuller
- R. Cowie
- Panagiotis Tzirakis
- Mohammad Soleymani
- Maximilian Schmitt
- Georgios N. Yannakakis
Citations in the corpus
- The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems (2018, background): “A more recent way of representing emotions is based on ranking which prefer a relative preference method to annotate emotions rather than labeling them with absolute values [25].”
- AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition (2019, background): “ently biased [79].”
- Privileged Information for Modeling Affect In The Wild (2021, background): “Another possible extension of this work is to test and compare ordinal learning methods [43], [46] for deriving models of affect through privileged information.”
- An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era (2022, background): “A solution is given through exploiting the inherently ordinal nature of emotions [160].”