  In this work, we investigate a recently proposed regularization technique
based on multi-branch architectures, called Shake-Shake regularization, for the
task of speech emotion recognition. In addition, we also propose variants to
incorporate domain knowledge into model configurations. The experimental
results demonstrate: $1)$ independently shaking sub-bands delivers favorable
models compared to shaking the entire spectral-temporal feature maps. $2)$ with
proper patience in early stopping, the proposed models can simultaneously
outperform the baseline and maintain a smaller performance gap between training
and validation.
