Speech emotion recognition aims to identify and analyze emotional states in
target speech similar to humans. Perfect emotion recognition can greatly
benefit a wide range of human-machine interaction tasks. Inspired by the human
process of understanding emotions, we demonstrate that compared to quantized
modeling, understanding speech content from a continuous perspective, akin to
human-like comprehension, enables the model to capture more comprehensive
emotional information. Additionally, considering that humans adjust their
perception of emotional words in textual semantic based on certain cues present
in speech, we design a novel search space and search for the optimal fusion
strategy for the two types of information. Experimental results further
validate the significance of this perception adjustment. Building on these
observations, we propose a novel framework called Multiple perspectives Fusion
Architecture Search (MFAS). Specifically, we utilize continuous-based knowledge
to capture speech semantic and quantization-based knowledge to learn textual
semantic. Then, we search for the optimal fusion strategy for them.
Experimental results demonstrate that MFAS surpasses existing models in
comprehensively capturing speech emotion information and can automatically
adjust fusion strategy.

本研究提出了一种多个视角融合框架，旨在解决语音情感识别中的多维感知问题，并能自动调整信息融合策略。通过利用连续模型和量化模型的优势，实现全面捕捉语音情感信息。