  This report describes the NPU-HC speaker verification system submitted to the
O-COCOSDA Multi-lingual Speaker Verification (MSV) Challenge 2022, which
focuses on developing speaker verification systems for low-resource Asian
languages. We participate in the I-MSV track, which aims to develop speaker
verification systems for various Indian languages. In this challenge, we first
explore different neural network frameworks for low-resource speaker
verification. Then we leverage vanilla fine-tuning and weight transfer
fine-tuning to transfer the out-domain pre-trained models to the in-domain
Indian dataset. Specifically, the weight transfer fine-tuning aims to constrain
the distance of the weights between the pre-trained model and the fine-tuned
model, which takes advantage of the previously acquired discriminative ability
from the large-scale out-domain datasets and avoids catastrophic forgetting and
overfitting at the same time. Finally, score fusion is adopted to further
improve performance. Together with the above contributions, we obtain 0.223%
EER on the public evaluation set, ranking 2nd place on the leaderboard. On the
private evaluation set, the EER of our submitted system is 2.123% and 0.630%
for the constrained and unconstrained sub-tasks of the I-MSV track, leading to
the 1st and 3rd place in the ranking, respectively.
