Anchored projection recovers a 2D arousal-valence plane from categorical labels

Zhou, Zhang & Duan fine-tune WavLM-Large on categorical speech-emotion classification only (never continuous arousal-valence labels), then map its features into a 2D arousal-valence plane via anchored dimensionality reduction [zhou-etal-2023-learning-arousal-valence-representation-categorical-emotion-labels-speech] Each categorical emotion is first pinned to its known arousal-valence coordinate from psychology (Russell & Mehrabian), then a weighted kNN-graph, UMAP-style optimization refines each utterance around its anchor [zhou-etal-2023-learning-arousal-valence-representation-categorical-emotion-labels-speech] The learned structure is a 2D plane with two continuous axes (neutral at the origin), not a closed-loop circumplex with S^1 cyclic topology [zhou-etal-2023-learning-arousal-valence-representation-categorical-emotion-labels-speech] On IEMOCAP the method reaches CCC up to 0.674 (valence) and 0.679 (arousal), matching or exceeding fully-supervised AV regression (0.317 valence baseline) without any continuous AV labels [zhou-etal-2023-learning-arousal-valence-representation-categorical-emotion-labels-speech] Cluster-center MAE to the psychological anchors (0.141-0.376 across IEMOCAP, EmoDB, MEAD) confirms alignment to the Russell axes rather than an arbitrary 2D layout [zhou-etal-2023-learning-arousal-valence-representation-categorical-emotion-labels-speech] An ablation removing the anchors (plain unsupervised UMAP) collapses CCC to near zero, so the imposed anchors, not the graph optimization, create the alignment, making any emergent/recoverable framing an over-claim [zhou-etal-2023-learning-arousal-valence-representation-categorical-emotion-labels-speech]