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The SJTU X-LANCE Lab System for CNSRC 2022

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arxiv 2206.11699 v5 pith:XOCZ5DN7 submitted 2022-06-23 cs.SD eess.AS

Zhengyang Chen , Bei Liu , Bing Han , Leying Zhang , Yanmin Qian This is my paper · ORCID
classification cs.SDeess.AS
keywords systemcnsrcspeakerachievechallengeresultsjtusystems
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. Memory-Efficient Training for Deep Speaker Embedding Learning in Speaker Verification

    eess.AS 2024-12 conditional novelty 4.0 of 10

    A combination of reversible residual blocks and 8-bit optimizer state quantization trains deep speaker embedding extractors with up to 16.2x less GPU memory and comparable accuracy.

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