To train machine learning algorithms to predict emotional expressions in terms of arousal and valence, annotated datasets are needed. However, as different people perceive others' emotional expressions differently, their annotations are subjective. To account for this, annotations are typically collected from multiple annotators and averaged to obtain ground-truth labels. However, when exclusively trained on this averaged ground-truth, the model is agnostic to the inherent subjectivity in emotional expressions. In this work, we therefore propose an end-to-end Bayesian neural network capable of being trained on a distribution of annotations to also capture the subjectivity-based label uncertainty. Instead of a Gaussian, we model the annotation distribution using Student's t-distribution, which also accounts for the number of annotations available. We derive the corresponding Kullback-Leibler divergence loss and use it to train an estimator for the annotation distribution, from which the mean and uncertainty can be inferred. We validate the proposed method using two in-the-wild datasets. We show that the proposed t-distribution based approach achieves state-of-the-art uncertainty modeling results in speech emotion recognition, and also consistent results in cross-corpora evaluations. Furthermore, analyses reveal that the advantage of a t-distribution over a Gaussian grows with increasing inter-annotator correlation and a decreasing number of annotations available.

翻译：为训练机器学习算法预测以唤醒度和效价维度表示的情感表达，需要标注数据集。然而，由于不同个体对他人情感表达的感知存在差异，其标注结果具有主观性。为应对这一问题，通常收集多位标注者的标注结果，并通过平均化处理获得基准真实标签。然而，当模型仅基于这种平均化的基准标签进行训练时，会丧失对情感表达固有主观性的感知能力。为此，本文提出一种端到端贝叶斯神经网络，该网络可基于标注分布进行训练，从而同时捕捉由主观性导致的标签不确定性。不同于高斯分布，我们采用学生t分布对标注分布进行建模，该分布能有效反映可用标注数量的影响。我们推导了对应的Kullback-Leibler散度损失函数，并利用其训练标注分布估计器，从而推断出均值与不确定性。通过两个野外数据集验证了所提方法的有效性。实验表明，我们提出的基于t分布的方法在语音情感识别中取得了最先进的不确定性建模结果，并在跨语料库评估中保持了一致性表现。此外，分析揭示：t分布相较于高斯分布的优势会随着标注者间相关性的增强及可用标注数量的减少而增大。