Enhanced-Deep-Residual-Shrinkage-Network-Based Voiceprint Recognition in the Electric Industry

Abstract

Voiceprint recognition can extract voice features and identity the speaker through the voice information, which has great application prospects in personnel identity verification and voice dispatching in the electric industry. The traditional voiceprint recognition algorithms work well in a quiet environment. However, noise interference inevitably exists in the electric industry, degrading the accuracy of traditional voiceprint recognition algorithms. In this paper, we propose an enhanced deep residual shrinkage network (EDRSN)-based voiceprint recognition by combining the traditional voiceprint recognition algorithms with deep learning (DL) in the context of the noisy electric industry environment, where a dual-path convolution recurrent network (DPCRN) is employed to reduce the noise, and its structure is also improved based on the deep residual shrinkage network (DRSN). Moreover, we further use a convolutional block attention mechanism (CBAM) module and a hybrid dilated convolution (HDC) in the proposed EDRSN. Simulation results show that the proposed network can enhance the speaker’s vocal features and further distinguish and eliminate the noise features, thus reducing the noise influence and achieving better recognition performance in a noisy electric environment.