Publication detail

Audio Enhancing With DNN Autoencoder For Speaker Recognition

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

Original Title

Audio Enhancing With DNN Autoencoder For Speaker Recognition

Type

conference paper

Language

English

Original Abstract

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.

Keywords

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

Authors

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

Released

20. 3. 2016

Publisher

IEEE Signal Processing Society

Location

Shanghai

ISBN

978-1-4799-9988-0

Book

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

Pages from

5090

Pages to

5094

Pages count

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/"
}