A ROBUST TEXT-INDEPENDENT SPEAKER VERIFICATION METHOD BASED ON SPEECH SEPARATION AND DEEP SPEAKER

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.