A framework converts interpretable facial and acoustic features into language descriptions, feeds them to a pretrained LM for semantic embeddings, and uses those embeddings as priors to improve valence and arousal change prediction on Aff-Wild2 and SEWA while remaining transparent.
An analyst-inspector framework for evaluating reproducibility of llms in data science.arXiv e-prints, pages arXiv–2502, 2025
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LaScA: Language-Conditioned Scalable Modelling of Affective Dynamics
A framework converts interpretable facial and acoustic features into language descriptions, feeds them to a pretrained LM for semantic embeddings, and uses those embeddings as priors to improve valence and arousal change prediction on Aff-Wild2 and SEWA while remaining transparent.