# End-to-end DNN Based Speaker Recognition Inspired by i-vector and PLDA

arXiv: https://arxiv.org/abs/1710.02369 · HTML: https://mlsanity.com/paper/1710.02369/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [A Speaker Verification Backend with Robust Performance across Conditions](https://mlsanity.com/paper/2102.01760/) (2021)
- [A Discriminative Hierarchical PLDA-based Model for Spoken Language Recognition](https://mlsanity.com/paper/2201.01364/) (2022)
- [Improved Relation Networks for End-to-End Speaker Verification and Identification](https://mlsanity.com/paper/2203.17218/) (2022)
- [Enhancing Quantised End-to-End ASR Models via Personalisation](https://mlsanity.com/paper/2309.09136/) (2023)

## Abstract (arXiv)

Recently several end-to-end speaker verification systems based on deep neural networks (DNNs) have been proposed. These systems have been proven to be competitive for text-dependent tasks as well as for text-independent tasks with short utterances. However, for text-independent tasks with longer utterances, end-to-end systems are still outperformed by standard i-vector + PLDA systems. In this work, we develop an end-to-end speaker verification system that is initialized to mimic an i-vector + PLDA baseline. The system is then further trained in an end-to-end manner but regularized so that it does not deviate too far from the initial system. In this way we mitigate overfitting which normally limits the performance of end-to-end systems. The proposed system outperforms the i-vector + PLDA baseline on both long and short duration utterances.