# Explore Long-Range Context feature for Speaker Verification

arXiv: https://arxiv.org/abs/2112.07134 · HTML: https://mlsanity.com/paper/2112.07134/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2021.

## Built on by (most cited first, 1 of 1)

- [Integrated Multi-Level Knowledge Distillation for Enhanced Speaker Verification](https://mlsanity.com/paper/2409.09389/) (2024)

## Abstract (arXiv)

Capturing long-range dependency and modeling long temporal contexts is proven to benefit speaker verification tasks. In this paper, we propose the combination of the Hierarchical-Split block(HS-block) and the Depthwise Separable Self-Attention(DSSA) module to capture richer multi-range context speaker features from a local and global perspective respectively. Specifically, the HS-block splits the feature map and filters into several groups and stacks them in one block, which enlarges the receptive fields(RFs) locally. The DSSA module improves the multi-head self-attention mechanism by the depthwise-separable strategy and explicit sparse attention strategy to model the pairwise relations globally and captures effective long-range dependencies in each channel. Experiments are conducted on the Voxceleb and SITW. Our best system achieves 1.27% EER on the Voxceleb1 test set and 1.56% on SITW by applying the combination of HS-block and DSSA module.