Detail publikace

Audio Enhancing With DNN Autoencoder For Speaker Recognition

PLCHOT, O. BURGET, L. ARONOWITZ, H. MATĚJKA, P.

Originální název

Audio Enhancing With DNN Autoencoder For Speaker Recognition

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In this paper we present a design of a DNN-based autoencoder for speech enhancement and its use for speaker recognition systems for distant microphones and noisy data. We started with augmenting the Fisher database with artificially noised and reverberated data and trained the autoencoder to map noisy and reverberated speech to its clean version. We use the autoencoder as a preprocessing step in the later stage of modelling in state-of-the-art text-dependent and text-independent speaker recognition systems. We report relative improvements up to 50% for the text-dependent system and up to 48% for the text-independent one. With text-independent system, we present a more detailed analysis on various conditions of NIST SRE 2010 and PRISM suggesting that the proposed preprocessig is a promising and efficient way to build a robust speaker recognition system for distant microphone and noisy data.

Klíčová slova

speaker recognition, denoising, de-reverberation, neural networks, DNN

Autoři

PLCHOT, O.; BURGET, L.; ARONOWITZ, H.; MATĚJKA, P.

Vydáno

20. 3. 2016

Nakladatel

IEEE Signal Processing Society

Místo

Shanghai

ISBN

978-1-4799-9988-0

Kniha

Proceedings of the 41th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), 2016

Strany od

5090

Strany do

5094

Strany počet

5

URL

https://www.fit.vut.cz/research/publication/11139/

BibTex

@inproceedings{BUT130961,
  author="Oldřich {Plchot} and Lukáš {Burget} and Hagai {Aronowitz} and Pavel {Matějka}",
  title="Audio Enhancing With DNN Autoencoder For Speaker Recognition",
  booktitle="Proceedings of the 41th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), 2016",
  year="2016",
  pages="5090--5094",
  publisher="IEEE Signal Processing Society",
  address="Shanghai",
  doi="10.1109/ICASSP.2016.7472647",
  isbn="978-1-4799-9988-0",
  url="https://www.fit.vut.cz/research/publication/11139/"
}