A robust speaker recognition based on Data augmentation

Abstract It is known that large-scale training data can get the better effect of recognition. However, it is difficult to collect a lot of labeled training data for speaker recognition. At the same time, the performance of speaker recognition is greatly influenced by environmental noise. In this paper, we use data augmentation by adding noise to get much training data and improve the robustness of speaker recognition. The experimental results demonstrate that data augmentation have the better performance improvement on Chinese-863 database.