# What and When to Learn: CURriculum Ranking Loss for Large-Scale Speaker Verification

arXiv: https://arxiv.org/abs/2603.24432 · HTML: https://mlsanity.com/paper/2603.24432/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training](https://mlsanity.com/paper/2108.06209/) (2021)
- [EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation](https://mlsanity.com/paper/2406.06185/) (2024)
- [Reshape Dimensions Network for Speaker Recognition](https://mlsanity.com/paper/2407.18223/) (2024)
- [VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification Benchmark](https://mlsanity.com/paper/2407.11510/) (2024)
- [Curriculum learning for self-supervised speaker verification](https://mlsanity.com/paper/2203.14525/) (2022)
- [SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions](https://mlsanity.com/paper/2509.17091/) (2025)
- [TidyVoice: A Curated Multilingual Dataset for Speaker Verification Derived from Common Voice](https://mlsanity.com/paper/2601.16358/) (2026)
- [Large-scale learning of generalised representations for speaker recognition](https://mlsanity.com/paper/2210.10985/) (2022)
- [PDAF: A Phonetic Debiasing Attention Framework For Speaker Verification](https://mlsanity.com/paper/2409.05799/) (2024)
- [Enhancing Speaker Verification with w2v-BERT 2.0 and Knowledge Distillation guided Structured Pruning](https://mlsanity.com/paper/2510.04213/) (2025)
- [CAARMA: Class Augmentation with Adversarial Mixup Regularization](https://mlsanity.com/paper/2503.16718/) (2025)
- [Human Voice is Unique](https://mlsanity.com/paper/2506.18182/) (2025)
- [DELULU: Discriminative Embedding Learning Using Latent Units for Speaker-Aware Self-Trained Speech Foundational Model](https://mlsanity.com/paper/2510.17662/) (2025)

## Built on by (most cited first, 1 of 1)

- [CoLMbo-SV: A Grounded Language Model for Explainable Speaker Verification](https://mlsanity.com/paper/2609.33212/) (2026)

## Abstract (arXiv)

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 largest-scale speaker verification system trained to date. Evaluated on VoxCeleb1-O, and SITW, Curry reduces EER by 86.8% and 60.0% over the Sub-center ArcFace baseline, establishing a new paradigm for robust speaker verification on imperfect large-scale data.