# Experimenting with Additive Margins for Contrastive Self-Supervised Speaker Verification

arXiv: https://arxiv.org/abs/2306.03664 · HTML: https://mlsanity.com/paper/2306.03664/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 14 of 14)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [In defence of metric learning for speaker recognition](https://mlsanity.com/paper/2003.11982/) (2020)
- [Utterance-level Aggregation For Speaker Recognition In The Wild](https://mlsanity.com/paper/1902.10107/) (2019)
- [Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning](https://mlsanity.com/paper/2012.07178/) (2020)
- [Large Margin Softmax Loss for Speaker Verification](https://mlsanity.com/paper/1904.03479/) (2019)
- [Augmentation adversarial training for self-supervised speaker recognition](https://mlsanity.com/paper/2007.12085/) (2020)
- [Delving into VoxCeleb: environment invariant speaker recognition](https://mlsanity.com/paper/1910.11238/) (2019)
- [Self-supervised speaker embeddings](https://mlsanity.com/paper/1904.03486/) (2019)
- [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)
- [Learning Speaker Embedding from Text-to-Speech](https://mlsanity.com/paper/2010.11221/) (2020)

## Built on by (most cited first, 3 of 3)

- [Additive Margin in Contrastive Self-Supervised Frameworks to Learn Discriminative Speaker Representations](https://mlsanity.com/paper/2404.14913/) (2024)
- [Whisper Speaker Identification: Leveraging Pre-Trained Multilingual Transformers for Robust Speaker Embeddings](https://mlsanity.com/paper/2503.10446/) (2025)
- [Self-Supervised Learning for Speaker Recognition: A study and review](https://mlsanity.com/paper/2602.10829/) (2026)

## Abstract (arXiv)

Most state-of-the-art self-supervised speaker verification systems rely on a contrastive-based objective function to learn speaker representations from unlabeled speech data. We explore different ways to improve the performance of these methods by: (1) revisiting how positive and negative pairs are sampled through a "symmetric" formulation of the contrastive loss; (2) introducing margins similar to AM-Softmax and AAM-Softmax that have been widely adopted in the supervised setting. We demonstrate the effectiveness of the symmetric contrastive loss which provides more supervision for the self-supervised task. Moreover, we show that Additive Margin and Additive Angular Margin allow reducing the overall number of false negatives and false positives by improving speaker separability. Finally, by combining both techniques and training a larger model we achieve 7.50% EER and 0.5804 minDCF on the VoxCeleb1 test set, which outperforms other contrastive self supervised methods on speaker verification.