  Although deep neural networks are successful for many tasks in the speech
domain, the high computational and memory costs of deep neural networks make it
difficult to directly deploy highperformance Neural Network systems on
low-resource embedded devices. There are several mechanisms to reduce the size
of the neural networks i.e. parameter pruning, parameter quantization, etc.
This paper focuses on how to apply binary neural networks to the task of
speaker verification. The proposed binarization of training parameters can
largely maintain the performance while significantly reducing storage space
requirements and computational costs. Experiment results show that, after
binarizing the Convolutional Neural Network, the ResNet34-based network
achieves an EER of around 5% on the Voxceleb1 testing dataset and even
outperforms the traditional real number network on the text-dependent dataset:
Xiaole while having a 32x memory saving.
