  Deep speaker embedding has achieved state-of-the-art performance in speaker
recognition. A potential problem of these embedded vectors (called `x-vectors')
are not Gaussian, causing performance degradation with the famous PLDA back-end
scoring. In this paper, we propose a regularization approach based on
Variational Auto-Encoder (VAE). This model transforms x-vectors to a latent
space where mapped latent codes are more Gaussian, hence more suitable for PLDA
scoring.
