Publication detail

On the use of DNN Autoencoder for Robust Speaker Recognition

NOVOTNÝ, O. MATĚJKA, P. PLCHOT, O. GLEMBEK, O.

Original Title

On the use of DNN Autoencoder for Robust Speaker Recognition

Type

report

Language

English

Original Abstract

In this paper, we present an analysis of a DNN-based autoencoderfor speech enhancement, dereverberation and denoising.The target application is a robust speaker recognition system.We started with augmenting the Fisher database with artificiallynoised and reverberated data and we trained the autoencoderto map noisy and reverberated speech to its clean version.We use the autoencoder as a preprocessing step for a stateof-the-art text-independent speaker recognition system. Wecompare results achieved with pure autoencoder enhancement,multi-condition PLDA training and their simultaneous use. Wepresent a detailed analysis with various conditions of NIST SRE2010, PRISM and artificially corrupted NIST SRE 2010 telephonecondition. We conclude that the proposed preprocessingsignificantly outperforms the baseline and that this techniquecan be used to build a robust speaker recognition system forreverberated and noisy data.

Keywords

speaker recognition, signal enhancement, autoencoder

Authors

NOVOTNÝ, O.; MATĚJKA, P.; PLCHOT, O.; GLEMBEK, O.

Released

8. 11. 2018

Publisher

Faculty of Information Technology BUT

Location

Brno

Pages from

1

Pages to

5

Pages count

5

URL

https://www.fit.vut.cz/research/publication/11855/

BibTex

@techreport{BUT161935,
  author="Ondřej {Novotný} and Pavel {Matějka} and Oldřich {Plchot} and Ondřej {Glembek}",
  title="On the use of DNN Autoencoder for Robust Speaker Recognition",
  year="2018",
  publisher="Faculty of Information Technology BUT",
  address="Brno",
  pages="1--5",
  url="https://www.fit.vut.cz/research/publication/11855/"
}

Documents

novotny_2018_tech-rep-dnn-autoencoder.pdf