Estimating the Uncertainty in Emotion Attributes using Deep Evidential
  Regression
Wen Wu Chao Zhang Philip C. Woodland
In automatic emotion recognition (AER), labels assigned by different human
annotators to the same utterance are often inconsistent due to the inherent
complexity of emotion and the subjectivity of perception. Though deterministic
labels generated by averaging or voting are often used as the ground truth, it
ignores the intrinsic uncertainty revealed by the inconsistent labels. This
paper proposes a Bayesian approach, deep evidential emotion regression (DEER),
to estimate the uncertainty in emotion attributes. Treating the emotion
attribute labels of an utterance as samples drawn from an unknown Gaussian
distribution, DEER places an utterance-specific normal-inverse gamma prior over
the Gaussian likelihood and predicts its hyper-parameters using a deep neural
network model. It enables a joint estimation of emotion attributes along with
the aleatoric and epistemic uncertainties. AER experiments on the widely used
MSP-Podcast and IEMOCAP datasets showed DEER produced state-of-the-art results
for both the mean values and the distribution of emotion attributes.