Beijing ZKJ-NPU Speaker Verification System for VoxCeleb Speaker Recognition Challenge 2021
Li Zhang, Huan Zhao, Qinling Meng, Yanli Chen, Min Liu, Lei Xie

TL;DR
This paper presents the Beijing ZKJ-NPU system for VoxCeleb Speaker Recognition Challenge 2021, utilizing advanced neural networks and normalization techniques to achieve second place in both tracks.
Contribution
Introduction of novel CNN-based models like ResNet-DTCF, CoAtNet, and PyConv for improved speaker verification performance.
Findings
Achieved top-tier performance with minDCF/EER of 0.1205/2.8160% and 0.1175/2.8400%.
Fused multiple systems to enhance accuracy.
Secured second place in both challenge tracks.
Abstract
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.
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Taxonomy
TopicsSpeech Recognition and Synthesis · Speech and Audio Processing · Music and Audio Processing
