Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challenge2020
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.
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Data AugmentationDenoisingSpeaker RecognitionSpeaker VerificationMethods 이 논문이 사용한 방법론
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