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InfoResearchPeer-reviewed
Cross-Corpus Speech Emotion Recognition Based on Dynamically Filtering Multistage Diffusion Model
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Summary
The authors propose DFMDM, a dynamically filtering multistage diffusion model for cross-corpus speech emotion recognition. It runs a three-stage loop of generation, filtering and enhancement, where an adversarial domain classifier guides pseudosample generation and a dynamic filtering module removes low-quality samples that deviate from target domain features. The model reports significant gains over state-of-the-art approaches on CASIA, EmoDB, eNTERFACE and IEMOCAP.