  The scarcity of labeled far-field speech is a constraint for training
superior far-field speaker verification systems. Fine-tuning the model
pre-trained on large-scale near-field speech substantially outperforms training
from scratch. However, the fine-tuning method suffers from two
limitations--catastrophic forgetting and overfitting. In this paper, we propose
a weight transfer regularization(WTR) loss to constrain the distance of the
weights between the pre-trained model with large-scale near-field speech and
the fine-tuned model through a small number of far-field speech. With the WTR
loss, the fine-tuning process takes advantage of the previously acquired
discriminative ability from the large-scale near-field speech without
catastrophic forgetting. Meanwhile, we use the PAC-Bayes generalization theory
to analyze the generalization bound of the fine-tuned model with the WTR loss.
The analysis result indicates that the WTR term makes the fine-tuned model have
a tighter generalization upper bound. Moreover, we explore three kinds of norm
distance for weight transfer, which are L1-norm distance, L2-norm distance and
Max-norm distance. Finally, we evaluate the effectiveness of the WTR loss on
VoxCeleb (pre-trained dataset) and FFSVC (fine-tuned dataset) datasets.
