  Speech emotion recognition (SER) is a key technology to enable more natural
human-machine communication. However, SER has long suffered from a lack of
public large-scale labeled datasets. To circumvent this problem, we investigate
how unsupervised representation learning on unlabeled datasets can benefit SER.
We show that the contrastive predictive coding (CPC) method can learn salient
representations from unlabeled datasets, which improves emotion recognition
performance. In our experiments, this method achieved state-of-the-art
concordance correlation coefficient (CCC) performance for all emotion
primitives (activation, valence, and dominance) on IEMOCAP. Additionally, on
the MSP- Podcast dataset, our method obtained considerable performance
improvements compared to baselines.
