# The ins and outs of speaker recognition: lessons from VoxSRC 2020

arXiv: https://arxiv.org/abs/2010.15809 · HTML: https://mlsanity.com/paper/2010.15809/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [DDDM-VC: Decoupled Denoising Diffusion Models with Disentangled Representation and Prior Mixup for Verified Robust Voice Conversion](https://mlsanity.com/paper/2305.15816/) (2023)
- [HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech Synthesis](https://mlsanity.com/paper/2311.12454/) (2023)
- [Diff-HierVC: Diffusion-based Hierarchical Voice Conversion with Robust Pitch Generation and Masked Prior for Zero-shot Speaker Adaptation](https://mlsanity.com/paper/2311.04693/) (2023)
- [EmoSphere-TTS: Emotional Style and Intensity Modeling via Spherical Emotion Vector for Controllable Emotional Text-to-Speech](https://mlsanity.com/paper/2406.07803/) (2024)
- [Disentangling Voice and Content with Self-Supervision for Speaker Recognition](https://mlsanity.com/paper/2310.01128/) (2023)
- [MFA: TDNN with Multi-scale Frequency-channel Attention for Text-independent Speaker Verification with Short Utterances](https://mlsanity.com/paper/2202.01624/) (2022)
- [VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2302.10248/) (2023)
- [HierVST: Hierarchical Adaptive Zero-shot Voice Style Transfer](https://mlsanity.com/paper/2307.16171/) (2023)
- [The VoxCeleb Speaker Recognition Challenge: A Retrospective](https://mlsanity.com/paper/2408.14886/) (2024)
- [Multi-scale speaker embedding-based graph attention networks for speaker diarisation](https://mlsanity.com/paper/2110.03361/) (2021)
- [Towards single integrated spoofing-aware speaker verification embeddings](https://mlsanity.com/paper/2305.19051/) (2023)
- [Singer Identity Representation Learning using Self-Supervised Techniques](https://mlsanity.com/paper/2401.05064/) (2024)
- [Adapting Speaker Embeddings for Speaker Diarisation](https://mlsanity.com/paper/2104.02879/) (2021)
- [t-EER: Parameter-Free Tandem Evaluation of Countermeasures and Biometric Comparators](https://mlsanity.com/paper/2309.12237/) (2023)
- [Residual Information in Deep Speaker Embedding Architectures](https://mlsanity.com/paper/2302.02742/) (2023)
- [A Study on Bias and Fairness In Deep Speaker Recognition](https://mlsanity.com/paper/2303.08026/) (2023)
- [LASPA: Language Agnostic Speaker Disentanglement with Prefix-Tuned Cross-Attention](https://mlsanity.com/paper/2506.02083/) (2025)
- [The ReturnZero System for VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2209.10147/) (2022)
- [Interpretable Spectrum Transformation Attacks to Speaker Recognition](https://mlsanity.com/paper/2302.10686/) (2023)
- [Voxceleb-ESP: preliminary experiments detecting Spanish celebrities from their voices](https://mlsanity.com/paper/2401.09441/) (2023)
- [SEED: Speaker Embedding Enhancement Diffusion Model](https://mlsanity.com/paper/2505.16798/) (2025)

## Abstract (arXiv)

The VoxCeleb Speaker Recognition Challenge (VoxSRC) at Interspeech 2020 offers a challenging evaluation for speaker recognition systems, which includes celebrities playing different parts in movies. The goal of this work is robust speaker recognition of utterances recorded in these challenging environments. We utilise variants of the popular ResNet architecture for speaker recognition and perform extensive experiments using a range of loss functions and training parameters. To this end, we optimise an efficient training framework that allows powerful models to be trained with limited time and resources. Our trained models demonstrate improvements over most existing works with lighter models and a simple pipeline. The paper shares the lessons learned from our participation in the challenge.