  Speech Emotion Recognition (SER) is an important research topic in
human-computer interaction. Many recent works focus on directly extracting
emotional cues through pre-trained knowledge, frequently overlooking
considerations of appropriateness and comprehensiveness. Therefore, we propose
a novel framework for pre-training knowledge in SER, called Multi-perspective
Fusion Search Network (MFSN). Considering comprehensiveness, we partition
speech knowledge into Textual-related Emotional Content (TEC) and
Speech-related Emotional Content (SEC), capturing cues from both semantic and
acoustic perspectives, and we design a new architecture search space to fully
leverage them. Considering appropriateness, we verify the efficacy of different
modeling approaches in capturing SEC and fills the gap in current research.
Experimental results on multiple datasets demonstrate the superiority of MFSN.
