---
_id: '11813'
abstract:
- lang: eng
  text: 'The parametric Bayesian Feature Enhancement (BFE) and a datadriven Denoising
    Autoencoder (DA) both bring performance gains in severe single-channel speech
    recognition conditions. The first can be adjusted to different conditions by an
    appropriate parameter setting, while the latter needs to be trained on conditions
    similar to the ones expected at decoding time, making it vulnerable to a mismatch
    between training and test conditions. We use a DNN backend and study reverberant
    ASR under three types of mismatch conditions: different room reverberation times,
    different speaker to microphone distances and the difference between artificially
    reverberated data and the recordings in a reverberant environment. We show that
    for these mismatch conditions BFE can provide the targets for a DA. This unsupervised
    adaptation provides a performance gain over the direct use of BFE and even enables
    to compensate for the mismatch of real and simulated reverberant data.'
author:
- first_name: Jahn
  full_name: Heymann, Jahn
  id: '9168'
  last_name: Heymann
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
- first_name: P.
  full_name: Golik, P.
  last_name: Golik
- first_name: R.
  full_name: Schlueter, R.
  last_name: Schlueter
citation:
  ama: 'Heymann J, Haeb-Umbach R, Golik P, Schlueter R. Unsupervised adaptation of
    a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under
    mismatch conditions. In: <i>Acoustics, Speech and Signal Processing (ICASSP),
    2015 IEEE International Conference On</i>. ; 2015:5053-5057. doi:<a href="https://doi.org/10.1109/ICASSP.2015.7178933">10.1109/ICASSP.2015.7178933</a>'
  apa: Heymann, J., Haeb-Umbach, R., Golik, P., &#38; Schlueter, R. (2015). Unsupervised
    adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant
    asr under mismatch conditions. In <i>Acoustics, Speech and Signal Processing (ICASSP),
    2015 IEEE International Conference on</i> (pp. 5053–5057). <a href="https://doi.org/10.1109/ICASSP.2015.7178933">https://doi.org/10.1109/ICASSP.2015.7178933</a>
  bibtex: '@inproceedings{Heymann_Haeb-Umbach_Golik_Schlueter_2015, title={Unsupervised
    adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant
    asr under mismatch conditions}, DOI={<a href="https://doi.org/10.1109/ICASSP.2015.7178933">10.1109/ICASSP.2015.7178933</a>},
    booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International
    Conference on}, author={Heymann, Jahn and Haeb-Umbach, Reinhold and Golik, P.
    and Schlueter, R.}, year={2015}, pages={5053–5057} }'
  chicago: Heymann, Jahn, Reinhold Haeb-Umbach, P. Golik, and R. Schlueter. “Unsupervised
    Adaptation of a Denoising Autoencoder by Bayesian Feature Enhancement for Reverberant
    Asr under Mismatch Conditions.” In <i>Acoustics, Speech and Signal Processing
    (ICASSP), 2015 IEEE International Conference On</i>, 5053–57, 2015. <a href="https://doi.org/10.1109/ICASSP.2015.7178933">https://doi.org/10.1109/ICASSP.2015.7178933</a>.
  ieee: J. Heymann, R. Haeb-Umbach, P. Golik, and R. Schlueter, “Unsupervised adaptation
    of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr
    under mismatch conditions,” in <i>Acoustics, Speech and Signal Processing (ICASSP),
    2015 IEEE International Conference on</i>, 2015, pp. 5053–5057.
  mla: Heymann, Jahn, et al. “Unsupervised Adaptation of a Denoising Autoencoder by
    Bayesian Feature Enhancement for Reverberant Asr under Mismatch Conditions.” <i>Acoustics,
    Speech and Signal Processing (ICASSP), 2015 IEEE International Conference On</i>,
    2015, pp. 5053–57, doi:<a href="https://doi.org/10.1109/ICASSP.2015.7178933">10.1109/ICASSP.2015.7178933</a>.
  short: 'J. Heymann, R. Haeb-Umbach, P. Golik, R. Schlueter, in: Acoustics, Speech
    and Signal Processing (ICASSP), 2015 IEEE International Conference On, 2015, pp.
    5053–5057.'
date_created: 2019-07-12T05:28:45Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2015.7178933
keyword:
- codecs
- signal denoising
- speech recognition
- Bayesian feature enhancement
- denoising autoencoder
- reverberant ASR
- single-channel speech recognition
- speaker to microphone distances
- unsupervised adaptation
- Adaptation models
- Noise reduction
- Reverberation
- Speech
- Speech recognition
- Training
- deep neuronal networks
- denoising autoencoder
- feature enhancement
- robust speech recognition
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2015/hey_icassp_2015.pdf
oa: '1'
page: 5053-5057
publication: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International
  Conference on
status: public
title: Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement
  for reverberant asr under mismatch conditions
type: conference
user_id: '44006'
year: '2015'
...