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User: w-marciszewski

The SJTU X-LANCE Lab System for CNSRC 2022. (arXiv:2206.11699v5 [cs.SD] UPDATED)

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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.

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