Learning Robust Self-attention Features for Speech Emotion Recognition
  with Label-adaptive Mixup
Lei Kang Lichao Zhang Dazhi Jiang
Speech Emotion Recognition (SER) is to recognize human emotions in a natural
verbal interaction scenario with machines, which is considered as a challenging
problem due to the ambiguous human emotions. Despite the recent progress in
SER, state-of-the-art models struggle to achieve a satisfactory performance. We
propose a self-attention based method with combined use of label-adaptive mixup
and center loss. By adapting label probabilities in mixup and fitting center
loss to the mixup training scheme, our proposed method achieves a superior
performance to the state-of-the-art methods.