  Neural models, in particular the d-vector and x-vector architectures, have
produced state-of-the-art performance on many speaker verification tasks.
However, two potential problems of these neural models deserve more
investigation. Firstly, both models suffer from `information leak', which means
that some parameters participating in model training will be discarded during
inference, i.e, the layers that are used as the classifier. Secondly, these
models do not regulate the distribution of the derived speaker vectors. This
`unconstrained distribution' may degrade the performance of the subsequent
scoring component, e.g., PLDA. This paper proposes a Gaussian-constrained
training approach that (1) discards the parametric classifier, and (2) enforces
the distribution of the derived speaker vectors to be Gaussian. Our experiments
on the VoxCeleb and SITW databases demonstrated that this new training approach
produced more representative and regular speaker embeddings, leading to
consistent performance improvement.
