  Speech emotion recognition systems (SER) can achieve high accuracy when the
training and test data are identically distributed, but this assumption is
frequently violated in practice and the performance of SER systems plummet
against unforeseen data shifts. The design of robust models for accurate SER is
challenging, which limits its use in practical applications. In this paper we
propose a deeper neural network architecture wherein we fuse DenseNet, LSTM and
Highway Network to learn powerful discriminative features which are robust to
noise. We also propose data augmentation with our network architecture to
further improve the robustness. We comprehensively evaluate the architecture
coupled with data augmentation against (1) noise, (2) adversarial attacks and
(3) cross-corpus settings. Our evaluations on the widely used IEMOCAP and
MSP-IMPROV datasets show promising results when compared with existing studies
and state-of-the-art models.
