# Beijing ZKJ-NPU Speaker Verification System for VoxCeleb Speaker Recognition Challenge 2021

arXiv: https://arxiv.org/abs/2109.03568 · HTML: https://mlsanity.com/paper/2109.03568/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2021.

## Built on by (most cited first, 5 of 5)

- [VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2302.10248/) (2023)
- [The VoxCeleb Speaker Recognition Challenge: A Retrospective](https://mlsanity.com/paper/2408.14886/) (2024)
- [The ReturnZero System for VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2209.10147/) (2022)
- [Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System](https://mlsanity.com/paper/2508.20983/) (2025)
- [A SUPERB-Style Benchmark of Self-Supervised Speech Models for Audio Deepfake Detection](https://mlsanity.com/paper/2603.01482/) (2026)

## Abstract (arXiv)

In this report, we describe the Beijing ZKJ-NPU team submission to the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21). We participated in the fully supervised speaker verification track 1 and track 2. In the challenge, we explored various kinds of advanced neural network structures with different pooling layers and objective loss functions. In addition, we introduced the ResNet-DTCF, CoAtNet and PyConv networks to advance the performance of CNN-based speaker embedding model. Moreover, we applied embedding normalization and score normalization at the evaluation stage. By fusing 11 and 14 systems, our final best performances (minDCF/EER) on the evaluation trails are 0.1205/2.8160% and 0.1175/2.8400% respectively for track 1 and 2. With our submission, we came to the second place in the challenge for both tracks.