# LightCAM: A Fast and Light Implementation of Context-Aware Masking based D-TDNN for Speaker Verification

arXiv: https://arxiv.org/abs/2402.06073 · HTML: https://mlsanity.com/paper/2402.06073/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking](https://mlsanity.com/paper/2303.00332/) (2023)
- [BUT System Description to VoxCeleb Speaker Recognition Challenge 2019](https://mlsanity.com/paper/1910.12592/) (2019)
- [MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification](https://mlsanity.com/paper/2203.15249/) (2022)
- [PCF: ECAPA-TDNN with Progressive Channel Fusion for Speaker Verification](https://mlsanity.com/paper/2303.00204/) (2023)
- [Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification](https://mlsanity.com/paper/1902.07821/) (2019)
- [Build a SRE Challenge System: Lessons from VoxSRC 2022 and CNSRC 2022](https://mlsanity.com/paper/2211.00815/) (2022)

## Abstract (arXiv)

Traditional Time Delay Neural Networks (TDNN) have achieved state-of-the-art performance at the cost of high computational complexity and slower inference speed, making them difficult to implement in an industrial environment. The Densely Connected Time Delay Neural Network (D-TDNN) with Context Aware Masking (CAM) module has proven to be an efficient structure to reduce complexity while maintaining system performance. In this paper, we propose a fast and lightweight model, LightCAM, which further adopts a depthwise separable convolution module (DSM) and uses multi-scale feature aggregation (MFA) for feature fusion at different levels. Extensive experiments are conducted on VoxCeleb dataset, the comparative results show that it has achieved an EER of 0.83 and MinDCF of 0.0891 in VoxCeleb1-O, which outperforms the other mainstream speaker verification methods. In addition, complexity analysis further demonstrates that the proposed architecture has lower computational cost and faster inference speed.