  Speech Self-Supervised Learning (SSL) has demonstrated considerable efficacy
in various downstream tasks. Nevertheless, prevailing self-supervised models
often overlook the incorporation of emotion-related prior information, thereby
neglecting the potential enhancement of emotion task comprehension through
emotion prior knowledge in speech. In this paper, we propose an emotion-aware
speech representation learning with intensity knowledge. Specifically, we
extract frame-level emotion intensities using an established speech-emotion
understanding model. Subsequently, we propose a novel emotional masking
strategy (EMS) to incorporate emotion intensities into the masking process. We
selected two representative models based on Transformer and CNN, namely
MockingJay and Non-autoregressive Predictive Coding (NPC), and conducted
experiments on IEMOCAP dataset. Experiments have demonstrated that the
representations derived from our proposed method outperform the original model
in SER task.
