This technical report describes the SJTU X-LANCE Lab system for the three tracks in CNSRC 2022. In this challenge, we explored the speaker embedding modeling ability of deep ResNet (Deeper r-vector). All the systems are only trained on the Cnceleb training set and we use the same systems for the three tracks in CNSRC 2022. In this challenge, our system ranks the first place in the fixed track of speaker verification task. Our best single system and fusion system achieve 0.3164 and 0.2975 minDCF respectively. Besides, we submit the result of ResNet221 to the speaker retrieval track and achieve 0.4626 mAP.
翻译:本技术报告介绍了CNSRC 2022年三条轨道的SJTU X-Lance实验室系统。在这项挑战中,我们探讨了深ResNet(深 R-矢量器)嵌入模型能力的演讲人。所有系统都只接受Canceleb培训,我们在CNSRC 2022年的三条轨道上使用同样的系统。在这项挑战中,我们的系统排在发言者核查任务固定轨道的第一位。我们最好的单一系统和聚合系统分别达到0.3164和0.2975 minDCF。此外,我们把ResNet221的结果提交发言者检索轨道,并实现0.4626 mAP。