Emotion annotation is inherently subjective and cognitively demanding, producing signals that reflect diverse perceptions across annotators rather than a single ground truth. In continuous affect prediction, this variability is typically collapsed into point estimates such as the mean or median, discarding valuable information about annotator disagreement and uncertainty. In this work, we propose a distribution-aware framework that models annotation consensus using the Beta distribution. Instead of predicting a single affect value, models estimate the mean and standard deviation of the annotation distribution, which are transformed into valid Beta parameters through moment matching. This formulation enables the recovery of higher-order distributional descriptors, including skewness, kurtosis, and quantiles, in closed form. As a result, the model captures not only the central tendency of emotional perception but also variability, asymmetry, and uncertainty in annotator responses. We evaluate the proposed approach on the SEWA and RECOLA datasets using multimodal features. Experimental results show that Beta-based modelling produces predictive distributions that closely match the empirical annotator distributions while achieving competitive performance with conventional regression approaches. These findings highlight the importance of modelling annotation uncertainty in affective computing and demonstrate the potential of distribution-aware learning for subjective signal analysis.

翻译：情感标注本质上具有主观性和认知密集性，产生的信号反映了标注者之间多样化的感知，而非单一的真实标准。在连续情感预测中，这种变异性通常被压缩为均值或中位数等点估计，从而丢弃了关于标注者分歧和不确定性的有价值信息。在这项工作中，我们提出了一种分布感知框架，利用Beta分布对标注共识进行建模。该模型不预测单一情感值，而是估计标注分布的均值和标准差，并通过矩匹配将其转换为有效的Beta参数。这种公式化能够以闭式形式恢复高阶分布描述符，包括偏度、峰度和分位数。因此，模型不仅捕捉情感感知的中心趋势，还能捕捉标注者响应中的变异性、非对称性和不确定性。我们在SEWA和RECOLA数据集上使用多模态特征评估了所提出的方法。实验结果表明，基于Beta的建模产生的预测分布与经验标注分布高度吻合，同时实现了与常规回归方法相竞争的性能。这些发现突显了在情感计算中对标注不确定性进行建模的重要性，并展示了分布感知学习在主观信号分析中的潜力。