Detail publikace

On the use of DNN Autoencoder for Robust Speaker Recognition

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

Originální název

On the use of DNN Autoencoder for Robust Speaker Recognition

Typ

zpráva odborná

Jazyk

angličtina

Originální abstrakt

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 stateof- 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.

Klíčová slova

speaker recognition, signal enhancement, autoencoder

Autoři

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

Vydáno

8. 11. 2018

Nakladatel

Faculty of Information Technology BUT

Místo

Brno

Strany od

1

Strany do

5

Strany počet

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/"
}

Dokumenty

novotny_2018_tech-rep-dnn-autoencoder.pdf