A Beta distribution framework models annotator consensus in continuous affect prediction by estimating mean and variance parameters to recover variability, skewness, and quantiles.
End-to-end label uncer- tainty modeling for speech-based arousal recognition using bayesian neural networks.arXiv preprint arXiv:2110.03299
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Beyond the Mean: Modelling Annotation Distributions in Continuous Affect Prediction
A Beta distribution framework models annotator consensus in continuous affect prediction by estimating mean and variance parameters to recover variability, skewness, and quantiles.