  The x-vector maps segments of arbitrary duration to vectors of fixed
dimension using deep neural network. Combined with the probabilistic linear
discriminant analysis (PLDA) backend, the x-vector/PLDA has become the dominant
framework in text-independent speaker verification. Nevertheless, how to
extract the x-vector appropriate for the PLDA backend is a key problem. In this
paper, we propose a Gaussian noise constrained network (GNCN) to extract
xvector, which adopts a multi-task learning strategy with the primary task
classifying the speakers and the auxiliary task just fitting the Gaussian
noises. Experiments are carried out using the SITW database. The results
demonstrate the effectiveness of our proposed method
