LLM-Based Synthetic Ground Truth Generation for Audio-Based Emotion Classification via In-Context Learning
arXiv:2606.14784v1 Announce Type: cross Abstract: 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
The increasing sophistication of LLMs allows for their application in generating synthetic data, addressing the common challenge of data scarcity in developing robust AI models for complex tasks like emotion classification.
This development could significantly accelerate progress in human-computer interaction by enabling more reliable inference of human emotional and cognitive states, critical for advanced AI systems.
The ability to generate high-quality synthetic ground truth could reduce reliance on expensive and time-consuming manual data annotation for tasks like emotion classification from audio, fostering faster model development and deployment.
- · AI/ML researchers
- · Human-computer interaction developers
- · Virtual reality platforms
- · AI agent developers
- · Manual data annotation services (for emotion-based audio)
- · Companies reliant on traditional, large-scale human labeled datasets
More sophisticated AI models for understanding human emotions and cognitive states will emerge due to improved data generation.
Enhanced emotional intelligence in AI could lead to more empathetic and effective human-AI collaboration in various applications.
The proliferation of context-aware AI could further blur the lines between human and artificial interaction, potentially leading to new ethical considerations and societal norms.
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