  Deep speaker embedding has achieved satisfactory performance in speaker
verification. By enforcing the neural model to discriminate the speakers in the
training set, deep speaker embedding (called `x-vectors`) can be derived from
the hidden layers. Despite its good performance, the present embedding model is
highly domain sensitive, which means that it often works well in domains whose
acoustic condition matches that of the training data (in-domain), but degrades
in mismatched domains (out-of-domain). In this paper, we present a domain
adaptation approach based on Variational Auto-Encoder (VAE). This model
transforms x-vectors to a regularized latent space; within this latent space, a
small amount of data from the target domain is sufficient to accomplish the
adaptation. Our experiments demonstrated that by this VAE-adaptation approach,
speaker embeddings can be easily transformed to the target domain, leading to
noticeable performance improvement.
