Learning Annotation Consensus for Continuous Emotion Recognition
This addresses the issue of lost inter-rater variability in affective computing for emotion recognition, though it is incremental as it builds on existing multi-annotator methods.
The paper tackled the problem of multiple inconsistent annotations in continuous emotion recognition by proposing a multi-annotator training approach that seeks consensus across all annotators, resulting in outperformance over traditional methods that use a single unified label on RECOLA and COGNIMUSE datasets.
In affective computing, datasets often contain multiple annotations from different annotators, which may lack full agreement. Typically, these annotations are merged into a single gold standard label, potentially losing valuable inter-rater variability. We propose a multi-annotator training approach for continuous emotion recognition (CER) that seeks a consensus across all annotators rather than relying on a single reference label. Our method employs a consensus network to aggregate annotations into a unified representation, guiding the main arousal-valence predictor to better reflect collective inputs. Tested on the RECOLA and COGNIMUSE datasets, our approach outperforms traditional methods that unify annotations into a single label. This underscores the benefits of fully leveraging multi-annotator data in emotion recognition and highlights its applicability across various fields where annotations are abundant yet inconsistent.