LLM-Based Synthetic Ground Truth Generation for Audio-Based Emotion Classification via In-Context Learning
Understanding human states and interaction dynamics is a core goal of human-computer interaction (HCI). As interaction paradigms become more immersive, virtual reality (VR) has emerged as a powerful platform for studying collaborative work. In such settings, evaluating team collaboration states, including team performance and team resilience, requires continuous and reliable inference of latent team-level cognitive and affective states from multi-modal sensor data, such as speech signals. However, generating ground truth labels for these latent states remains challenging due to sensor-induced noise, contextual variability, and sparse expert annotations. Traditional self-reporting approach...
| Authors | Qing Huang, Pooja Pol, Jianing Zhang |
|---|---|
| Primary category | cs.SD |
| Published | 10 June 2026 |
| Last updated at source | 18 June 2026 |
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