Speech emotion recognition is an important aspect of humancomputer interaction. Prior work proposes various end-to-end
models to improve the classification performance. However,
most of them rely on the cross-entropy loss together with
softmax as the supervision component, which does not explicitly
encourage discriminative learning of features. In this paper, we
introduce the contrastive loss function to encourage intra-class
compactness and inter-class separability between learnable
features. Furthermore, multiple feature selection methods and
pairwise sample selection methods are evaluated. To verify the
performance of the proposed system, we conduct experiments on
The Interactive Emotional Dyadic Motion Capture (IEMOCAP)
database – a common evaluation corpus. Experimental results
reveal the advantages of the proposed method, which reaches
62.19% in the weighted accuracy and 63.21% in the unweighted
accuracy. It outperforms the baseline system that is optimized
without the contrastive loss function with 1.14% and 2.55% in the
weighted accuracy and the unweighted accuracy, respectively.