# The DKU-DukeECE Systems for VoxCeleb Speaker Recognition Challenge 2020

arXiv: https://arxiv.org/abs/2010.12731 · HTML: https://mlsanity.com/paper/2010.12731/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [The VoxCeleb Speaker Recognition Challenge: A Retrospective](https://mlsanity.com/paper/2408.14886/) (2024)
- [Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation](https://mlsanity.com/paper/2309.03019/) (2023)
- [UNISOUND System for VoxCeleb Speaker Recognition Challenge 2023](https://mlsanity.com/paper/2308.12526/) (2023)
- [Enhancing Speaker Verification with w2v-BERT 2.0 and Knowledge Distillation guided Structured Pruning](https://mlsanity.com/paper/2510.04213/) (2025)
- [CS-Rep: Making Speaker Verification Networks Embracing Re-parameterization](https://mlsanity.com/paper/2110.13465/) (2021)
- [TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding](https://mlsanity.com/paper/2203.09098/) (2022)
- [The ReturnZero System for VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2209.10147/) (2022)
- [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)
- [Self-Supervised Learning for Speaker Recognition: A study and review](https://mlsanity.com/paper/2602.10829/) (2026)

## Abstract (arXiv)

In this paper, we present the system submission for the VoxCeleb Speaker Recognition Challenge 2020 (VoxSRC-20) by the DKU-DukeECE team. For track 1, we explore various kinds of state-of-the-art front-end extractors with different pooling layers and objective loss functions. For track 3, we employ an iterative framework for self-supervised speaker representation learning based on a deep neural network (DNN). For track 4, we investigate the whole system pipeline for speaker diarization, including voice activity detection (VAD), uniform segmentation, speaker embedding extraction, and clustering.