The subjective perception of emotion leads to inconsistent labels from human annotators. Typically, utterances lacking majority-agreed labels are excluded when training an emotion classifier, which cause problems when encountering ambiguous emotional expressions during testing. This paper investigates three methods to handle ambiguous emotion. First, we show that incorporating utterances without majority-agreed labels as an additional class in the classifier reduces the classification performance of the other emotion classes. Then, we propose detecting utterances with ambiguous emotions as out-of-domain samples by quantifying the uncertainty in emotion classification using evidential deep learning. This approach retains the classification accuracy while effectively detects ambiguous emotion expressions. Furthermore, to obtain fine-grained distinctions among ambiguous emotions, we propose representing emotion as a distribution instead of a single class label. The task is thus re-framed from classification to distribution estimation where every individual annotation is taken into account, not just the majority opinion. The evidential uncertainty measure is extended to quantify the uncertainty in emotion distribution estimation. Experimental results on the IEMOCAP and CREMA-D datasets demonstrate the superior capability of the proposed method in terms of majority class prediction, emotion distribution estimation, and uncertainty estimation.

翻译：情感的主观感知导致人工标注者产生不一致的标签。通常，在训练情感分类器时，缺乏多数同意标签的语句会被排除，这在测试时遇到模糊情感表达时会引发问题。本文研究了处理模糊情感的三种方法。首先，我们表明，将缺乏多数同意标签的语句作为附加类别纳入分类器会降低其他情感类别的分类性能。然后，我们提出通过使用证据深度学习量化情感分类中的不确定性，将具有模糊情感的语句检测为域外样本。该方法在有效检测模糊情感表达的同时保持了分类准确性。此外，为获得模糊情感之间的细粒度区分，我们提出将情感表示为分布而非单一类别标签。因此，任务从分类重新定义为分布估计，其中考虑每一份个体标注，而不仅仅是多数意见。证据不确定性度量被扩展以量化情感分布估计中的不确定性。在IEMOCAP和CREMA-D数据集上的实验结果证明了所提方法在多数类别预测、情感分布估计和不确定性估计方面的优越能力。