Shaking Acoustic Spectral Sub-bands Can Better Regularize Learning in Affective Computing
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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.
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