The SpeakIn System for VoxCeleb Speaker Recognition Challange 2021

By Miao Zhao, Yufeng Ma, Min Liu, Minqiang Xu. 2021

69 citations recorded by the source corpus.

This report describes our submission to the track 1 and track 2 of the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC 2021). Both track 1 and track 2 share the same speaker verification system, which only uses VoxCeleb2-dev as our training set. This report explores several parts, including data augmentation, network structures, domain-based large margin fine-tuning, and back-end refinement. Our system is a fusion of 9 models and achieves first place in these two tracks of VoxSRC 2021. The minDCF of our submission is 0.1034, and the corresponding EER is 1.8460%.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jinyu Li
2. Shujie Liu
3. Zhuo Chen
4. Yu Wu
5. Takuya Yoshioka
6. Sanyuan Chen
7. Furu Wei
8. Jian Wu

In Signal Processing

1. Jinyu Li
2. Zhuo Chen
3. Takuya Yoshioka
4. Shujie Liu
5. Yu Wu
6. Sanyuan Chen
7. Naoyuki Kanda
8. Jian Wu

Citations in the corpus

• WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing (2021, background): “exceeds the winner system [56] (Vox1-O: 0.”
• Wespeaker: A Research and Production Oriented Speaker Embedding Learning Toolkit (2022, background): “The large margin fine-tuning strategy was first proposed in [26] and widely used in speaker verification challenge systems [19, 27, 28] to further enhance the system’s performance.”