LLM-Based Synthetic Ground Truth Generation for Audio-Based Emotion Classification via In-Context Learning
For researchers in HCI and VR studying team collaboration dynamics, this work provides a method to automate ground truth labeling, addressing the bottleneck of sparse expert annotations and static self-reports.
The paper tackles the challenge of generating synthetic ground truth labels for latent team-level cognitive and affective states from speech data in VR environments. It proposes an LLM-driven agentic workflow using in-context learning with few-shot demonstrations, achieving task adaptation comparable to fine-tuning without computational overhead.
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 approaches provide only static and delayed measurements and are therefore insufficient for capturing dynamic team processes reflected in continuous speech data. In this work, we propose a large language model (LLM)-driven, agentic inference workflow for automated emotion-related synthetic ground truth generation from streaming speech data in multi-user VR environments. Leveraging the generalization capabilities of LLMs, we use In-Context Learning (ICL) with few-shot demonstrations of paired audio-based samples and their corresponding transcriptions. ICL tends to achieve task adaptation comparable to model fine-tuning while circumventing the computational overhead of parameter updates. To construct informative and robust in-context prompts, we adopt a retrieval-based selection strategy that dynamically identifies relevant audio demonstrations based on similarity in the acoustic feature space.