论文标题
CNSRC 2022的SJTU X-LANCE实验室系统
The SJTU X-LANCE Lab System for CNSRC 2022
论文作者
论文摘要
该技术报告描述了CNSRC 2022中三个轨道的SJTU X-LANCE实验室系统。在此挑战中，我们探索了Deep Resnet（更深的R-vector）的扬声器嵌入建模能力。所有系统仅在CNCELEB培训集中训练，我们为CNSRC 2022中的三个轨道使用相同的系统。在这一挑战中，我们的系统在固定的扬声器验证任务中排名第一。我们最好的单个系统和融合系统分别达到0.3164和0.2975 MIDCF。此外，我们将RESNET221的结果提交给扬声器检索轨道并获得0.4626地图。更重要的是，我们帮助了AndeSpeaker [1]工具包复制我们的结果：https：//github.com/wenet-e2e/wespeaker。
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. More importantly, we have helped the wespeaker [1] toolkit reproduce our result: https://github.com/wenet-e2e/wespeaker.
