  Speech emotion recognition (SER) is the task of recognising human's emotional
states from speech. SER is extremely prevalent in helping dialogue systems to
truly understand our emotions and become a trustworthy human conversational
partner. Due to the lengthy nature of speech, SER also suffers from the lack of
abundant labelled data for powerful models like deep neural networks.
Pre-trained complex models on large-scale speech datasets have been
successfully applied to SER via transfer learning. However, fine-tuning complex
models still requires large memory space and results in low inference
efficiency. In this paper, we argue achieving a fast yet effective SER is
possible with self-distillation, a method of simultaneously fine-tuning a
pretrained model and training shallower versions of itself. The benefits of our
self-distillation framework are threefold: (1) the adoption of
self-distillation method upon the acoustic modality breaks through the limited
ground-truth of speech data, and outperforms the existing models' performance
on an SER dataset; (2) executing powerful models at different depth can achieve
adaptive accuracy-efficiency trade-offs on resource-limited edge devices; (3) a
new fine-tuning process rather than training from scratch for self-distillation
leads to faster learning time and the state-of-the-art accuracy on data with
small quantities of label information.
