Speech emotion recognition (SER) plays a crucial role in human-computer
interaction. The emergence of edge devices in the Internet of Things (IoT)
presents challenges in constructing intricate deep learning models due to
constraints in memory and computational resources. Moreover, emotional speech
data often contains private information, raising concerns about privacy leakage
during the deployment of SER models. To address these challenges, we propose a
data distillation framework to facilitate efficient development of SER models
in IoT applications using a synthesised, smaller, and distilled dataset. Our
experiments demonstrate that the distilled dataset can be effectively utilised
to train SER models with fixed initialisation, achieving performances
comparable to those developed using the original full emotional speech dataset.

通过合成、缩小和提炼数据集的方法，我们提出了一种数据蒸馏框架，以促进在物联网应用中高效开发情感语音识别模型。我们的实验表明，可以有效地利用提炼后的数据集来训练情感语音识别模型，并且性能与使用原始完整的情感语音数据集开发的模型相当。