In this paper, a novel architecture for speaker recognition is proposed by cascading speech enhancement and speaker processing. It aims to improve speaker recognition performance when speech signals are corrupted by noise. Instead of separately processing speech enhancement and speaker recognition, the two modules are integrated into one framework by a joint optimisation using deep neural networks. Furthermore, to increase the robustness against noise, a multi-stage attention mechanism is

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... ed to highlight the speaker related features learned from context information in both time and frequency domains. To evaluate speaker identification and verification performance of the proposed approach, VoxCeleb1, one of mostly used benchmark datasets, is used. Moreover, the robustness evaluation is also conducted on VoxCeleb1 when its being corrupted by three types of interferences, general noise, music, and babble, at different signal-to-noise ratio (SNR) levels. The obtained results show that the proposed approach using speech enhancement and multi-stage attention models outperforms two strong baselines in different acoustic conditions in our experiments.