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

翻译：时延神经网络（TDNN）已被证明在说话人确认任务中具有高效性。其成功变体之一ECAPA-TDNN在实现最先进性能的同时，付出了计算复杂度显著更高和推理速度更慢的代价，这使其难以适应推理速率要求高和计算资源受限的场景。因此，我们致力于寻找一种既能达到ECAPA-TDNN性能又能保持原始TDNN效率的网络架构。本文提出了一种基于上下文感知掩码的高效网络——CAM++，该网络以密集连接时延神经网络（D-TDNN）为主干，采用新颖的多粒度池化机制捕获不同层次的上下文信息。在两个公开基准数据集VoxCeleb和CN-Celeb上的大量实验表明，所提出的架构在降低计算开销和提升推理速度的同时，性能超越了其他主流说话人确认系统。