# Compact Speaker Embedding: lrx-vector

arXiv: https://arxiv.org/abs/2008.05011 · HTML: https://mlsanity.com/paper/2008.05011/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

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

- [Label-free Knowledge Distillation with Contrastive Loss for Light-weight Speaker Recognition](https://mlsanity.com/paper/2212.03090/) (2022)
- [Compression of end-to-end non-autoregressive image-to-speech system for low-resourced devices](https://mlsanity.com/paper/2312.00174/) (2023)
- [The Voiceprint Fallacy: Why Voices Are Not Unique Biometric Imprints](https://mlsanity.com/paper/2608.07980/) (2026)

## Abstract (arXiv)

Deep neural networks (DNN) have recently been widely used in speaker recognition systems, achieving state-of-the-art performance on various benchmarks. The x-vector architecture is especially popular in this research community, due to its excellent performance and manageable computational complexity. In this paper, we present the lrx-vector system, which is the low-rank factorized version of the x-vector embedding network. The primary objective of this topology is to further reduce the memory requirement of the speaker recognition system. We discuss the deployment of knowledge distillation for training the lrx-vector system and compare against low-rank factorization with SVD. On the VOiCES 2019 far-field corpus we were able to reduce the weights by 28% compared to the full-rank x-vector system while keeping the recognition rate constant (1.83% EER).