# Discriminative Speaker Representation via Contrastive Learning with Class-Aware Attention in Angular Space

arXiv: https://arxiv.org/abs/2210.16622 · HTML: https://mlsanity.com/paper/2210.16622/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2022.

## Built on by (most cited first, 2 of 2)

- [Non-Adaptive Adversarial Face Generation](https://mlsanity.com/paper/2507.12107/) (2025)
- [Hyperbolic Additive Margin Softmax with Hierarchical Information for Speaker Verification](https://mlsanity.com/paper/2601.19709/) (2026)

## Abstract (arXiv)

The challenges in applying contrastive learning to speaker verification (SV) are that the softmax-based contrastive loss lacks discriminative power and that the hard negative pairs can easily influence learning. To overcome the first challenge, we propose a contrastive learning SV framework incorporating an additive angular margin into the supervised contrastive loss in which the margin improves the speaker representation's discrimination ability. For the second challenge, we introduce a class-aware attention mechanism through which hard negative samples contribute less significantly to the supervised contrastive loss. We also employed gradient-based multi-objective optimization to balance the classification and contrastive loss. Experimental results on CN-Celeb and Voxceleb1 show that this new learning objective can cause the encoder to find an embedding space that exhibits great speaker discrimination across languages.