Geometric Second-Order Feature Correlation Learning for Self-Supervised Speech Emotion Recognition 事件

Geometric Second-Order Feature Correlation Learning for Self-Supervised Speech Emotion Recognition arXiv:2606.06550v1 Announce Type: cross Abstract: Self-supervised learning (SSL) yields powerful, context-rich representations for speech emotion recognition (SER), yet aggregating these representations into holistic descriptors remains a bottleneck. Conventional first-order aggregation implicitly assumes feature independence, which overlooks the latent Riemannian geometry and discards higher-orde