Tongji University Undergraduate Team For The Voxceleb Speaker Recognition Challenge2020

Abstract

In this report, we discribe the submission of Tongji University undergraduate team to the CLOSE track of the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020 at Interspeech 2020. We applied the RSBU-CW module to the ResNet34 framework to improve the denoising ability of the network and better complete the speaker verification task in a complex environment.We trained two variants of ResNet,used score fusion and data-augmentation methods to improve the performance of the model. Our fusion of two selected systems for the CLOSE track achieves 0.2973 DCF and 4.9700% EER on the challenge evaluation set.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyshen2020tongji

Related papers

• Tongji University Team For The Voxceleb Speaker Recognition Challenge 2020 (2020)0.00
• Beijing ZKJ-NPU Speaker Verification System For Voxceleb Speaker Recognition Challenge 2021 (2021)0.00
• The Xx205 System For The Voxceleb Speaker Recognition Challenge 2020 (2020)0.00
• Shanerun System Description To Voxceleb Speaker Recognition Challenge 2020 (2020)0.00
• The SJTU X-LANCE Lab System For CNSRC 2022 (2022)6.22
• The DKU-MSXF Speaker Verification System For The Voxceleb Speaker Recognition Challenge 2023 (2023)0.00
• The Kriston AI System For The Voxceleb Speaker Recognition Challenge 2022 (2022)0.00
• Clova Baseline System For The Voxceleb Speaker Recognition Challenge 2020 (2020)0.00