# UNISOUND System for VoxCeleb Speaker Recognition Challenge 2023

arXiv: https://arxiv.org/abs/2308.12526 · HTML: https://mlsanity.com/paper/2308.12526/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [RepVGG: Making VGG-style ConvNets Great Again](https://mlsanity.com/paper/2101.03697/) (2021)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [The SpeakIn System for VoxCeleb Speaker Recognition Challange 2021](https://mlsanity.com/paper/2109.01989/) (2021)
- [The IDLAB VoxCeleb Speaker Recognition Challenge 2020 System Description](https://mlsanity.com/paper/2010.12468/) (2020)
- [The DKU-DukeECE Systems for VoxCeleb Speaker Recognition Challenge 2020](https://mlsanity.com/paper/2010.12731/) (2020)
- [Multi-query multi-head attention pooling and Inter-topK penalty for speaker verification](https://mlsanity.com/paper/2110.05042/) (2021)

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

- [The VoxCeleb Speaker Recognition Challenge: A Retrospective](https://mlsanity.com/paper/2408.14886/) (2024)
- [Memory-Efficient Training for Text-Dependent SV with Independent Pre-trained Models](https://mlsanity.com/paper/2411.10828/) (2024)
- [Memory-Efficient Training for Deep Speaker Embedding Learning in Speaker Verification](https://mlsanity.com/paper/2412.01195/) (2024)

## Abstract (arXiv)

This report describes the UNISOUND submission for Track1 and Track2 of VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC 2023). We submit the same system on Track 1 and Track 2, which is trained with only VoxCeleb2-dev. Large-scale ResNet and RepVGG architectures are developed for the challenge. We propose a consistency-aware score calibration method, which leverages the stability of audio voiceprints in similarity score by a Consistency Measure Factor (CMF). CMF brings a huge performance boost in this challenge. Our final system is a fusion of six models and achieves the first place in Track 1 and second place in Track 2 of VoxSRC 2023. The minDCF of our submission is 0.0855 and the EER is 1.5880%.