TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech
With the proliferation of speech AI agents, understanding emotional entrainment in conversational interaction has become increasingly important. Emotional entrainment is shaped by social relationships and conversational context, influencing affective coordination over time. We introduce DyadEE, a dataset for emotional entrainment detection in dyadic speech interactions, containing both emotionally entrained conversations and synthetic interactions where entrainment is disrupted through partner swapping and emotion resynthesis. We further propose TRACE, a window-level framework that models dyadic interaction as ordered sequences of acoustic embeddings derived from emotion fine-tuned Whispe...
| Authors | Sathvik Manikantan Napa Ugandhar, Hao Zhang, Alison Gunzler et al. |
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| Primary category | cs.CL |
| Published | 29 June 2026 |
| Last updated at source | 3 July 2026 |
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