# Rep Works in Speaker Verification

arXiv: https://arxiv.org/abs/2110.09720 · HTML: https://mlsanity.com/paper/2110.09720/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2021.

## Built on by (most cited first, 3 of 3)

- [The SpeakIn Speaker Verification System for Far-Field Speaker Verification Challenge 2022](https://mlsanity.com/paper/2209.11625/) (2022)
- [TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding](https://mlsanity.com/paper/2203.09098/) (2022)
- [The ReturnZero System for VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2209.10147/) (2022)

## Abstract (arXiv)

Multi-branch convolutional neural network architecture has raised lots of attention in speaker verification since the aggregation of multiple parallel branches can significantly improve performance. However, this design is not efficient enough during the inference time due to the increase of model parameters and extra operations. In this paper, we present a new multi-branch network architecture RepSPKNet that uses a re-parameterization technique. With this technique, our backbone model contains an efficient VGG-like inference state while its training state is a complicated multi-branch structure. We first introduce the specific structure of RepVGG into speaker verification and propose several variants of this structure. The performance is evaluated on VoxCeleb-based test sets. We demonstrate that both the branch diversity and the branch capacity play important roles in RepSPKNet designing. Our RepSPKNet achieves state-of-the-art performance with a 1.5982% EER and a 0.1374 minDCF on VoxCeleb1-H.