  Performance degradation caused by language mismatch is a common problem when
applying a speaker verification system on speech data in different languages.
This paper proposes a domain transfer network, named EDITnet, to alleviate the
language-mismatch problem on speaker embeddings without requiring speaker
labels. The network leverages a conditional variational auto-encoder to
transfer embeddings from the target domain into the source domain. A
self-supervised learning strategy is imposed on the transferred embeddings so
as to increase the cosine distance between embeddings from different speakers.
In the training process of the EDITnet, the embedding extraction model is fixed
without fine-tuning, which renders the training efficient and low-cost.
Experiments on Voxceleb and CN-Celeb show that the embeddings transferred by
EDITnet outperform the un-transferred ones by around 30% with the
ECAPA-TDNN512. Performance improvement can also be achieved with other
embedding extraction models, e.g., TDNN, SE-ResNet34.
