Cross-Corpus Speech Emotion Recognition Based on Dynamically Filtering Multistage Diffusion Model

This paper addresses a problem in cross-corpus speech emotion recognition (SER), which is the task of detecting emotions in speech across different datasets that may have different recording conditions or speaker characteristics. Existing AI methods that generate artificial training samples often create low-quality or noisy data, which weakens the learning process. The authors propose a new model called DFMDM that improves sample quality through three stages: generating samples with target characteristics, filtering out poor-quality samples, and regenerating better samples by mixing good ones with real data.