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：事实证明,时间延迟神经网络(TDNN)对于发言者的核查是有效的。它的成功变体之一,即ECAPA-TDNNN,以更复杂的计算和较慢的推论速度为代价实现了最先进的性能。这使得它不足以适用于要求高推论率和有限计算资源的假设情景。因此,我们有兴趣找到一个能够实现ECAPA-TDNNN和香草TDNN效率的架构。在本文中,我们提议建立一个基于环境觉悟掩蔽的有效网络,即CAM++,利用连接的密集时间延迟神经网络(D-TDNN)作为主干线,并采用新的多光谱集,在不同级别收集背景信息。关于VoxCeleb和CN-Celeb这两个公共基准的广泛实验表明,拟议的结构以较低的计算成本和更快的推算速度优于其他主流发言者核查系统。</s>