Learning Annotation Consensus for Continuous Emotion Recognition

• URL: http://arxiv.org/abs/2505.21196v1
• Date: Tue, 27 May 2025 13:43:45 GMT
• Title: Learning Annotation Consensus for Continuous Emotion Recognition
• Abstract summary: In affective computing, datasets often contain multiple annotations from different annotators, which may lack full agreement.<n>We propose a multi-annotator training approach for continuous emotion recognition (CER) that seeks a consensus across all annotators.<n>Our method employs a consensus network to aggregate annotations into a unified representation, guiding the main arousal-valence predictor to better reflect collective inputs.
• Score: 6.944372188747803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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