ISCA Archive FFSVC 2022
ISCA Archive FFSVC 2022

ZXIC Speaker Verification System for FFSVC 2022 Challenge

Yuan Lei, Zhou Cao, Dehui Kong, Ke Xu

This paper presents the development of ZXIC speaker verification system submitted to the task 1 of Interspeech 2022 Far-Field Speaker Verification Challenge (FFSVC2022). Deep neural network based discriminative embeddings, such as x-vectors, have been shown to perform well in speaker verification tasks. In far-field speaker verification system, mismatch between training and testing data and mismatch between enrollment and authentication utterances impact the system performance a lot. To alleviate this mismatch and improve the system performance, in this paper we propose a novel multi-reader domain adaption learning framework based on asymmetric metric learning. In this challenge, we also explore advanced neural network based embedding extractor structures including ECAPA-TDNN and ResNet-SE. A number of experiments on these architectures show that our proposed method is effective and improves the systems performance a lot. The final submitted systems are the fusion of several models. In FFSVC2022, our best system achieves a minimum of the detection cost function (minDCF) of 0.511and an equal error rate (EER) of 4.409% on the evaluation set.

doi: 10.21437/FFSVC.2022-1

Cite as: Lei, Y., Cao, Z., Kong, D., Xu, K. (2022) ZXIC Speaker Verification System for FFSVC 2022 Challenge. Proc. The 2022 Far-field Speaker Verification Challenge (FFSVC2022), 1-5, doi: 10.21437/FFSVC.2022-1

  author={Yuan Lei and Zhou Cao and Dehui Kong and Ke Xu},
  title={{ZXIC Speaker Verification System for FFSVC 2022 Challenge}},
  booktitle={Proc. The 2022 Far-field Speaker Verification Challenge (FFSVC2022)},