# The IDLAB VoxCeleb Speaker Recognition Challenge 2020 System Description

arXiv: https://arxiv.org/abs/2010.12468 · HTML: https://mlsanity.com/paper/2010.12468/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 17 of 17)

- [LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech](https://mlsanity.com/paper/2309.05472/) (2023)
- [VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2302.10248/) (2023)
- [USTC-KXDIGIT System Description for ASVspoof5 Challenge](https://mlsanity.com/paper/2409.01695/) (2024)
- [Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations](https://mlsanity.com/paper/2208.05413/) (2022)
- [UNISOUND System for VoxCeleb Speaker Recognition Challenge 2023](https://mlsanity.com/paper/2308.12526/) (2023)
- [The Phonexia VoxCeleb Speaker Recognition Challenge 2021 System Description](https://mlsanity.com/paper/2109.02052/) (2021)
- [TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding](https://mlsanity.com/paper/2203.09098/) (2022)
- [Training speaker recognition systems with limited data](https://mlsanity.com/paper/2203.14688/) (2022)
- [Residual Information in Deep Speaker Embedding Architectures](https://mlsanity.com/paper/2302.02742/) (2023)
- [The JHU submission to VoxSRC-21: Track 3](https://mlsanity.com/paper/2109.13425/) (2021)
- [Self-supervised Reflective Learning through Self-distillation and Online Clustering for Speaker Representation Learning](https://mlsanity.com/paper/2401.01473/) (2024)
- [THUEE system description for NIST 2020 SRE CTS challenge](https://mlsanity.com/paper/2210.06111/) (2022)
- [Cluster-Guided Unsupervised Domain Adaptation for Deep Speaker Embedding](https://mlsanity.com/paper/2303.15944/) (2023)
- [Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification](https://mlsanity.com/paper/2305.12703/) (2023)
- [Rethinking Speaker Embeddings for Speech Generation: Sub-Center Modeling for Capturing Intra-Speaker Diversity](https://mlsanity.com/paper/2407.04291/) (2024)
- [Self-Supervised Learning for Speaker Recognition: A study and review](https://mlsanity.com/paper/2602.10829/) (2026)
- [Understanding Hyperspherical Geometry of ECAPA-TDNN Embedding and Its Impact on Zero-Shot Voice Conversion](https://mlsanity.com/paper/2609.24688/) (2026)

## Abstract (arXiv)

In this technical report we describe the IDLAB top-scoring submissions for the VoxCeleb Speaker Recognition Challenge 2020 (VoxSRC-20) in the supervised and unsupervised speaker verification tracks. For the supervised verification tracks we trained 6 state-of-the-art ECAPA-TDNN systems and 4 Resnet34 based systems with architectural variations. On all models we apply a large margin fine-tuning strategy, which enables the training procedure to use higher margin penalties by using longer training utterances. In addition, we use quality-aware score calibration which introduces quality metrics in the calibration system to generate more consistent scores across varying levels of utterance conditions. A fusion of all systems with both enhancements applied led to the first place on the open and closed supervised verification tracks. The unsupervised system is trained through contrastive learning. Subsequent pseudo-label generation by iterative clustering of the training embeddings allows the use of supervised techniques. This procedure led to the winning submission on the unsupervised track, and its performance is closing in on supervised training.