  Recently, deep neural networks (DNN) have been widely used in speaker
recognition area. In order to achieve fast response time and high accuracy, the
requirements for hardware resources increase rapidly. However, as the speaker
recognition application is often implemented on mobile devices, it is necessary
to maintain a low computational cost while keeping high accuracy in far-field
condition. In this paper, we apply structural sparsification on time-delay
neural networks (TDNN) to remove redundant structures and accelerate the
execution. On our targeted hardware, our model can remove 60% of parameters and
only slightly increasing equal error rate (EER) by 0.18% while our structural
sparse model can achieve more than 1.5x speedup.
