Speaker recognition model with robust front-end processing algorithm

Abstract

The end-to-end speaker recognition model has made a great breakthrough in the research field and practical application scenarios. However, in practical application, we often suffer the diversity of noise, and the interference of noise will affect the performance of speaker recognition and classification model, and the model usually degrades in unseen scenes with noise. In this paper, a speaker recognition model combined with front-end enhancement is proposed. The front-end enhancement model (DTLN or CRNN) is combined with the back-end speaker recognition model (Res2Net-GhostVLAD) to improve the robustness of the model against noisy scenes, and the generalization capability of the model is increased by the data augmentation method (SpecAugment). Our proposed method was trained on VoxCeleb and AISHELL datasets and tested on VoxCeleb datasets. The test results show that the proposed method significantly improves the performance of the speaker recognition model in noisy scenarios, and the relative improvement of different front-end models is 9% and 13%, respectively.