# Towards Lightweight Applications: Asymmetric Enroll-Verify Structure for Speaker Verification

arXiv: https://arxiv.org/abs/2110.04438 · HTML: https://mlsanity.com/paper/2110.04438/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2021.

## Built on by (most cited first, 6 of 6)

- [Universal speaker recognition encoders for different speech segments duration](https://mlsanity.com/paper/2210.16231/) (2022)
- [Label-free Knowledge Distillation with Contrastive Loss for Light-weight Speaker Recognition](https://mlsanity.com/paper/2212.03090/) (2022)
- [Distilling Multi-Level X-vector Knowledge for Small-footprint Speaker Verification](https://mlsanity.com/paper/2303.01125/) (2023)
- [Post-Training Embedding Alignment for Decoupling Enrollment and Runtime Speaker Recognition Models](https://mlsanity.com/paper/2401.12440/) (2024)
- [Contrastive Knowledge Distillation for Embedding Refinement in Personalized Speech Enhancement](https://mlsanity.com/paper/2601.16235/) (2026)
- [On Low-Bit Quantization Errors in Speaker Verification: Diagnostic and Mitigation](https://mlsanity.com/paper/2606.08078/) (2026)

## Abstract (arXiv)

With the development of deep learning, automatic speaker verification has made considerable progress over the past few years. However, to design a lightweight and robust system with limited computational resources is still a challenging problem. Traditionally, a speaker verification system is symmetrical, indicating that the same embedding extraction model is applied for both enrollment and verification in inference. In this paper, we come up with an innovative asymmetric structure, which takes the large-scale ECAPA-TDNN model for enrollment and the small-scale ECAPA-TDNNLite model for verification. As a symmetrical system, our proposed ECAPA-TDNNLite model achieves an EER of 3.07% on the Voxceleb1 original test set with only 11.6M FLOPS. Moreover, the asymmetric structure further reduces the EER to 2.31%, without increasing any computational costs during verification.