  Emotions are subjective constructs. Recent end-to-end speech emotion
recognition systems are typically agnostic to the subjective nature of
emotions, despite their state-of-the-art performance. In this work, we
introduce an end-to-end Bayesian neural network architecture to capture the
inherent subjectivity in the arousal dimension of emotional expressions. To the
best of our knowledge, this work is the first to use Bayesian neural networks
for speech emotion recognition. At training, the network learns a distribution
of weights to capture the inherent uncertainty related to subjective arousal
annotations. To this end, we introduce a loss term that enables the model to be
explicitly trained on a distribution of annotations, rather than training them
exclusively on mean or gold-standard labels. We evaluate the proposed approach
on the AVEC'16 dataset. Qualitative and quantitative analysis of the results
reveals that the proposed model can aptly capture the distribution of
subjective arousal annotations, with state-of-the-art results in mean and
standard deviation estimations for uncertainty modeling.
