  The x-vector based deep neural network (DNN) embedding systems have
demonstrated effectiveness for text-independent speaker verification. This
paper presents a multi-task learning architecture for training the speaker
embedding DNN with the primary task of classifying the target speakers, and the
auxiliary task of reconstructing the first- and higher-order statistics of the
original input utterance. The proposed training strategy aggregates both the
supervised and unsupervised learning into one framework to make the speaker
embeddings more discriminative and robust. Experiments are carried out using
the NIST SRE16 evaluation dataset and the VOiCES dataset. The results
demonstrate that our proposed method outperforms the original x-vector approach
with very low additional complexity added.
