# C3-DINO: Joint Contrastive and Non-contrastive Self-Supervised Learning for Speaker Verification

arXiv: https://arxiv.org/abs/2208.07446 · HTML: https://mlsanity.com/paper/2208.07446/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2022.

## Built on by (most cited first, 12 of 12)

- [What Do Self-Supervised Speech and Speaker Models Learn? New Findings From a Cross Model Layer-Wise Analysis](https://mlsanity.com/paper/2401.17632/) (2024)
- [Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision](https://mlsanity.com/paper/2406.11169/) (2024)
- [Towards Supervised Performance on Speaker Verification with Self-Supervised Learning by Leveraging Large-Scale ASR Models](https://mlsanity.com/paper/2406.02285/) (2024)
- [Self-supervised Learning on Camera Trap Footage Yields a Strong Universal Face Embedder](https://mlsanity.com/paper/2507.10552/) (2025)
- [Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling](https://mlsanity.com/paper/2501.17772/) (2025)
- [Investigation of Speaker Representation for Target-Speaker Speech Processing](https://mlsanity.com/paper/2410.11243/) (2024)
- [Towards the Next Frontier in Speech Representation Learning Using Disentanglement](https://mlsanity.com/paper/2407.02543/) (2024)
- [Pushing the Frontiers of Self-Distillation Prototypes Network with Dimension Regularization and Score Normalization](https://mlsanity.com/paper/2505.13826/) (2025)
- [SSPS: Self-Supervised Positive Sampling for Robust Self-Supervised Speaker Verification](https://mlsanity.com/paper/2505.14561/) (2025)
- [Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision](https://mlsanity.com/paper/2308.02774/) (2023)
- [Self-Supervised Learning for Speaker Recognition: A study and review](https://mlsanity.com/paper/2602.10829/) (2026)
- [What You Train Is What You Get: Gender Bias, Training Composition, and Post-Hoc Mitigation in Audio Deepfake Detection](https://mlsanity.com/paper/2607.09891/) (2026)

## Abstract (arXiv)

Self-supervised learning (SSL) has drawn an increased attention in the field of speech processing. Recent studies have demonstrated that contrastive learning is able to learn discriminative speaker embeddings in a self-supervised manner. However, base contrastive self-supervised learning (CSSL) assumes that the pairs generated from a view of anchor instance and any view of other instances are all negative, which introduces many false negative pairs in constructing the loss function. The problem is referred as class-collision, which remains as one major issue that impedes the CSSL based speaker verification (SV) systems from achieving better performances. In the meanwhile, studies reveal that negative sample free SSL frameworks perform well in learning speaker or image representations. In this study, we investigate SSL techniques that lead to an improved SV performance. We first analyse the impact of false negative pairs in the CSSL systems. Then, a multi-stage Class-Collision Correction (C3) method is proposed, which leads to the state-of-the-art CSSL based speaker embedding system. On the basis of the pretrained CSSL model, we further propose to employ a negative sample free SSL objective (i.e., DINO) to fine-tune the speaker embedding network. The resulting speaker embedding system (C3-DINO) achieves 2.5% EER with a simple Cosine Distance Scoring method on Voxceleb1 test set, which outperforms the previous SOTA SSL system (4.86%) by a significant +45% relative improvement. With speaker clustering and pseudo labeling on Voxceleb2 training set, a LDA/CDS back-end applying on the C3-DINO speaker embeddings is able to further push the EER to 2.2%. Comprehensive experimental investigations of the Voxceleb benchmarks and our internal dataset demonstrate the effectiveness of our proposed methods, and the performance gap between the SSL SV and the supervised counterpart narrows further.