MSP-Podcast

Transformer embeddings shown to intrinsically encode Russell's circumplex model of emotion geometry

A new arXiv paper investigates whether Transformer-based text and speech encoders (RoBERTa, wav2vec 2.0) recover the geometric structure of Russell's circumplex model of affect — a valence-arousal topology from psychology. Experiments on naturalistic datasets (MSP-Podcast) and LLM-generated stimuli show that multimodal fusion achieves perfect topological alignment with Russell's primary emotion ordering, and zero-shot generic text embeddings place fine-grained emotion terms near their human-mapped coordinates. The authors argue this structure is intrinsically encoded in the representations rather than being an artifact of labeling, bridging psychological theory and representation learning.