  Emotion recognition is a key attribute for artificial intelligence systems
that need to naturally interact with humans. However, the task definition is
still an open problem due to the inherent ambiguity of emotions. In this paper,
a novel Bayesian training loss based on per-utterance Dirichlet prior
distributions is proposed for verbal emotion recognition, which models the
uncertainty in one-hot labels created when human annotators assign the same
utterance to different emotion classes. An additional metric is used to
evaluate the performance by detection test utterances with high labelling
uncertainty. This removes a major limitation that emotion classification
systems only consider utterances with labels where the majority of annotators
agree on the emotion class. Furthermore, a frequentist approach is studied to
leverage the continuous-valued "soft" labels obtained by averaging the one-hot
labels. We propose a two-branch model structure for emotion classification on a
per-utterance basis, which achieves state-of-the-art classification results on
the widely used IEMOCAP dataset. Based on this, uncertainty estimation
experiments were performed. The best performance in terms of the area under the
precision-recall curve when detecting utterances with high uncertainty was
achieved by interpolating the Bayesian training loss with the Kullback-Leibler
divergence training loss for the soft labels. The generality of the proposed
approach was verified using the MSP-Podcast dataset which yielded the same
pattern of results.
