  Meta-learning (ML) has recently become a research hotspot in speaker
verification (SV). We introduce two methods to improve the meta-learning
training for SV in this paper. For the first method, a backbone embedding
network is first jointly trained with the conventional cross entropy loss and
prototypical networks (PN) loss. Then, inspired by speaker adaptive training in
speech recognition, additional transformation coefficients are trained with
only the PN loss. The transformation coefficients are used to modify the
original backbone embedding network in the x-vector extraction process.
Furthermore, the random erasing (RE) data augmentation technique is applied to
all support samples in each episode to construct positive pairs, and a
contrastive loss between the augmented and the original support samples is
added to the objective in model training. Experiments are carried out on the
Speaker in the Wild (SITW) and VOiCES databases. Both of the methods can obtain
consistent improvements over existing meta-learning training frameworks. By
combining these two methods, we can observe further improvements on these two
databases.
