  Speaker embeddings become growing popular in the text-independent speaker
verification task. In this paper, we propose two improvements during the
training stage. The improvements are both based on triplet cause the training
stage and the evaluation stage of the baseline x-vector system focus on
different aims. Firstly, we introduce triplet loss for optimizing the Euclidean
distances between embeddings while minimizing the multi-class cross entropy
loss. Secondly, we design an embedding similarity measurement network for
controlling the similarity between the two selected embeddings. We further
jointly train the two new methods with the original network and achieve
state-of-the-art. The multi-task training synergies are shown with a 9%
reduction equal error rate (EER) and detected cost function (DCF) on the 2016
NIST Speaker Recognition Evaluation (SRE) Test Set.

Audio and Speech Processing;Computation and Language;Machine Learning;Sound