# SSPS: Self-Supervised Positive Sampling for Robust Self-Supervised Speaker Verification

arXiv: https://arxiv.org/abs/2505.14561 · HTML: https://mlsanity.com/paper/2505.14561/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 32 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 24 of 24)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2104.14548/) (2021)
- [In defence of metric learning for speaker recognition](https://mlsanity.com/paper/2003.11982/) (2020)
- [Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning](https://mlsanity.com/paper/2012.07178/) (2020)
- [Augmentation adversarial training for self-supervised speaker recognition](https://mlsanity.com/paper/2007.12085/) (2020)
- [Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization](https://mlsanity.com/paper/2112.04459/) (2021)
- [Pushing the limits of self-supervised speaker verification using regularized distillation framework](https://mlsanity.com/paper/2211.04168/) (2022)
- [C3-DINO: Joint Contrastive and Non-contrastive Self-Supervised Learning for Speaker Verification](https://mlsanity.com/paper/2208.07446/) (2022)
- [Self-Supervised Learning with Cluster-Aware-DINO for High-Performance Robust Speaker Verification](https://mlsanity.com/paper/2304.05754/) (2023)
- [Label-Efficient Self-Supervised Speaker Verification With Information Maximization and Contrastive Learning](https://mlsanity.com/paper/2207.05506/) (2022)
- [Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech](https://mlsanity.com/paper/2208.05445/) (2022)
- [Curriculum learning for self-supervised speaker verification](https://mlsanity.com/paper/2203.14525/) (2022)
- [Self-Supervised Training of Speaker Encoder with Multi-Modal Diverse Positive Pairs](https://mlsanity.com/paper/2210.15385/) (2022)
- [Self-Supervised Speaker Verification Using Dynamic Loss-Gate and Label Correction](https://mlsanity.com/paper/2208.01928/) (2022)
- [Unsupervised Representation Learning for Speaker Recognition via Contrastive Equilibrium Learning](https://mlsanity.com/paper/2010.11433/) (2020)
- [GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning](https://mlsanity.com/paper/2401.01990/) (2024)
- [Additive Margin in Contrastive Self-Supervised Frameworks to Learn Discriminative Speaker Representations](https://mlsanity.com/paper/2404.14913/) (2024)
- [Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling](https://mlsanity.com/paper/2501.17772/) (2025)

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

- [Self-Supervised Learning for Speaker Recognition: A study and review](https://mlsanity.com/paper/2602.10829/) (2026)

## Abstract (arXiv)

Self-Supervised Learning (SSL) has led to considerable progress in Speaker Verification (SV). The standard framework uses same-utterance positive sampling and data-augmentation to generate anchor-positive pairs of the same speaker. This is a major limitation, as this strategy primarily encodes channel information from the recording condition, shared by the anchor and positive. We propose a new positive sampling technique to address this bottleneck: Self-Supervised Positive Sampling (SSPS). For a given anchor, SSPS aims to find an appropriate positive, i.e., of the same speaker identity but a different recording condition, in the latent space using clustering assignments and a memory queue of positive embeddings. SSPS improves SV performance for both SimCLR and DINO, reaching 2.57% and 2.53% EER, outperforming SOTA SSL methods on VoxCeleb1-O. In particular, SimCLR-SSPS achieves a 58% EER reduction by lowering intra-speaker variance, providing comparable performance to DINO-SSPS.