An Energy and Memory Efficient Speaker Verification System Based on Binary Neural Networks

Speaker verification based on deep neural network is one of the most prospective biometric authentication methods. But massive computation and parameters make it difficult to deploy speaker verification systems on memory and power constrained embedded devices. Binary neural networks with weights and activations compressed to 1 bit can reduce computational and memory cost significantly. However, speaker verification system based on binary neural networks suffers performance degradation problem. In this paper, to mitigate this problem and facilitate information flow across layers, we optimize binary neural network structure by adding a shortcut to connect layers. We propose an energy and memory efficient speaker system based on the optimized binary networks. Experimental results on text-dependent and text-independent datasets show that performance of proposed speaker verification system outperforms conventional binary neural networks significantly. Compared with full precision networks, the proposed system can largely maintain the performance while reducing memory and computational costs.