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Time delay neural network (TDNN) has been proven to be efficient for speakerverification. One of its successful variants, ECAPA-TDNN, achievedstate-of-the-art performance at the cost of much higher computationalcomplexity and slower inference speed. This makes it inadequate for scenarioswith demanding inference rate and limited computational resources. We are thusinterested in finding an architecture that can achieve the performance ofECAPA-TDNN and the efficiency of vanilla TDNN. In this paper, we propose anefficient network based on context-aware masking, namely CAM++, which usesdensely connected time delay neural network (D-TDNN) as backbone and adopts anovel multi-granularity pooling to capture contextual information at differentlevels. Extensive experiments on two public benchmarks, VoxCeleb and CN-Celeb,demonstrate that the proposed architecture outperforms other mainstream speakerverification systems with lower computational cost and faster inference speed.

CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking