Kavli Affiliate: Zhuo Li
| First 5 Authors: Zhuo Li, Runqiu Xiao, Hangting Chen, Zhenduo Zhao, Zihan Zhang
| Summary:
This paper describes the systems developed by the HCCL team for the NIST 2021
speaker recognition evaluation (NIST SRE21).We first explore various
state-of-the-art speaker embedding extractors combined with a novel circle loss
to obtain discriminative deep speaker embeddings. Considering that
cross-channel and cross-linguistic speaker recognition are the key challenges
of SRE21, we introduce several techniques to reduce the cross-domain mismatch.
Specifically, Codec and speech enhancement are directly applied to the raw
speech to eliminate the codecs and the environment noise mismatch. We denote
the methods that work directly on speech to eliminate the relatively explicit
mismatches collectively as data adaptation methods. Experiments show that data
adaption methods achieve 15% improvements over our baseline. Furthermore, some
popular back-ends domain adaptation algorithms are deployed on speaker
embeddings to alleviate speaker performance degradation caused by the implicit
mismatch. Score calibration is a major failure for us in SRE21. The reason is
that score calibration with too many parameters easily lead to overfitting
problems.
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