# On the use of DNN Autoencoder for Robust Speaker Recognition

arXiv: https://arxiv.org/abs/1811.02938 · HTML: https://mlsanity.com/paper/1811.02938/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Task-aware Warping Factors in Mask-based Speech Enhancement](https://mlsanity.com/paper/2108.12128/) (2021)
- [Improved far-field speech recognition using Joint Variational Autoencoder](https://mlsanity.com/paper/2204.11286/) (2022)

## Abstract (arXiv)

In this paper, we present an analysis of a DNN-based autoencoder for speech enhancement, dereverberation and denoising. The target application is a robust speaker recognition system. We started with augmenting the Fisher database with artificially noised and reverberated data and we trained the autoencoder to map noisy and reverberated speech to its clean version. We use the autoencoder as a preprocessing step for a state-of-the-art text-independent speaker recognition system. We compare results achieved with pure autoencoder enhancement, multi-condition PLDA training and their simultaneous use. We present a detailed analysis with various conditions of NIST SRE 2010, PRISM and artificially corrupted NIST SRE 2010 telephone condition. We conclude that the proposed preprocessing significantly outperforms the baseline and that this technique can be used to build a robust speaker recognition system for reverberated and noisy data.