  Speech emotion recognition (SER) is essential for enhancing human-computer
interaction in speech-based applications. Despite improvements in specific
emotional datasets, there is still a research gap in SER's capability to
generalize across real-world situations. In this paper, we investigate
approaches to generalize the SER system across different emotion datasets. In
particular, we incorporate 11 emotional speech datasets and illustrate a
comprehensive benchmark on the SER task. We also address the challenge of
imbalanced data distribution using over-sampling methods when combining SER
datasets for training. Furthermore, we explore various evaluation protocols for
adeptness in the generalization of SER. Building on this, we explore the
potential of Whisper for SER, emphasizing the importance of thorough
evaluation. Our approach is designed to advance SER technology by integrating
speaker-independent methods.
