What and When to Learn: CURriculum Ranking Loss for Large-Scale Speaker Verification
Speaker verification at large scale remains an open challenge as fixed-margin losses treat all samples equally regardless of quality. We hypothesize that mislabeled or degraded samples introduce noisy gradients that disrupt compact speaker manifolds. We propose Curry (CURriculum Ranking), an adaptive loss that estimates sample difficulty online via Sub-center ArcFace: confidence scores from dominant sub-center cosine similarity rank samples into easy, medium, and hard tiers using running batch statistics, without auxiliary annotations. Learnable weights guide the model from stable identity foundations through manifold refinement to boundary sharpening. To our knowledge, this is the larges...
| Authors | Massa Baali, Sarthak Bisht, Rita Singh et al. |
|---|---|
| Primary category | cs.SD |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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