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.

通过运用证据深度学习的方法，本文研究了处理模糊情感的三种方法。我们发现将没有多数一致标签的话语作为分类器的额外类别会降低其他情感类别的分类性能。然后，我们提出通过量化情感分类的不确定性来检测具有模糊情感的话语，将其视为域外样本。这种方法可以保持分类准确性同时有效地检测模糊情感表达。此外，为了获得模糊情感的细粒度区分，我们提出以分布形式表示情感，而非单一类别标签。因此，任务从分类重新定义为分布估计，考虑每个个体标注而非仅仅多数意见。证据不确定性度量方法被扩展用于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出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卓越能力。