A Robust Text-independent Speaker Verification Method Based on Speech Separation and Deep Speaker

Abstract

Recently, deep neural networks (DNNs) have achieved incredible performance in speaker verification. However, most of which remains sensitive to environment noise. In this paper, we propose an end-to-end speaker verification framework to enhance the robustness against background noise. The proposed framework first utilizes convolutional recurrent network (CRN) to address speech separation. Then the output of the middle layer of the CRN is used as the auxiliary feature, and together with the robust Filter banks (Fbanks) feature of noisy speech are fed to the speaker verification system. The speech separation and speaker verification are jointly optimized. Compared with deep speaker and DNN/i-vector, systematic evaluation indicates that the proposed algorithm can obtain a better performance in noisy conditions.