DyadEE

TRACE framework and DyadEE dataset for emotional entrainment detection in dyadic speech

Researchers introduce DyadEE, a dataset for detecting emotional entrainment in dyadic speech, containing both natural entrained conversations and synthetic disrupted interactions created via partner swapping and emotion resynthesis. They also propose TRACE, a window-level framework that models dyadic interactions as ordered sequences of acoustic embeddings from emotion fine-tuned Whisper representations. TRACE achieves 97.01% accuracy on DyadEE, with conversational context and relationship information proving key to performance. The work is motivated by the growing deployment of speech AI agents that need to understand affective coordination.