---
_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'
...
---
_id: '11820'
abstract:
- lang: eng
  text: In this paper, we derive an uncertainty decoding rule for automatic speech
    recognition (ASR), which accounts for both corrupted observations and inter-frame
    correlation. The conditional independence assumption, prevalent in hidden Markov
    model-based ASR, is relaxed to obtain a clean speech posterior that is conditioned
    on the complete observed feature vector sequence. This is a more informative posterior
    than one conditioned only on the current observation. The novel decoding is used
    to obtain a transmission-error robust remote ASR system, where the speech capturing
    unit is connected to the decoder via an error-prone communication network. We
    show how the clean speech posterior can be computed for communication links being
    characterized by either bit errors or packet loss. Recognition results are presented
    for both distributed and network speech recognition, where in the latter case
    common voice-over-IP codecs are employed.
author:
- first_name: Valentin
  full_name: Ion, Valentin
  last_name: Ion
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Ion V, Haeb-Umbach R. A Novel Uncertainty Decoding Rule With Applications to
    Transmission Error Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech,
    and Language Processing</i>. 2008;16(5):1047-1060. doi:<a href="https://doi.org/10.1109/TASL.2008.925879">10.1109/TASL.2008.925879</a>
  apa: Ion, V., &#38; Haeb-Umbach, R. (2008). A Novel Uncertainty Decoding Rule With
    Applications to Transmission Error Robust Speech Recognition. <i>IEEE Transactions
    on Audio, Speech, and Language Processing</i>, <i>16</i>(5), 1047–1060. <a href="https://doi.org/10.1109/TASL.2008.925879">https://doi.org/10.1109/TASL.2008.925879</a>
  bibtex: '@article{Ion_Haeb-Umbach_2008, title={A Novel Uncertainty Decoding Rule
    With Applications to Transmission Error Robust Speech Recognition}, volume={16},
    DOI={<a href="https://doi.org/10.1109/TASL.2008.925879">10.1109/TASL.2008.925879</a>},
    number={5}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
    author={Ion, Valentin and Haeb-Umbach, Reinhold}, year={2008}, pages={1047–1060}
    }'
  chicago: 'Ion, Valentin, and Reinhold Haeb-Umbach. “A Novel Uncertainty Decoding
    Rule With Applications to Transmission Error Robust Speech Recognition.” <i>IEEE
    Transactions on Audio, Speech, and Language Processing</i> 16, no. 5 (2008): 1047–60.
    <a href="https://doi.org/10.1109/TASL.2008.925879">https://doi.org/10.1109/TASL.2008.925879</a>.'
  ieee: V. Ion and R. Haeb-Umbach, “A Novel Uncertainty Decoding Rule With Applications
    to Transmission Error Robust Speech Recognition,” <i>IEEE Transactions on Audio,
    Speech, and Language Processing</i>, vol. 16, no. 5, pp. 1047–1060, 2008.
  mla: Ion, Valentin, and Reinhold Haeb-Umbach. “A Novel Uncertainty Decoding Rule
    With Applications to Transmission Error Robust Speech Recognition.” <i>IEEE Transactions
    on Audio, Speech, and Language Processing</i>, vol. 16, no. 5, 2008, pp. 1047–60,
    doi:<a href="https://doi.org/10.1109/TASL.2008.925879">10.1109/TASL.2008.925879</a>.
  short: V. Ion, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
    Processing 16 (2008) 1047–1060.
date_created: 2019-07-12T05:28:53Z
date_updated: 2022-01-06T06:51:10Z
department:
- _id: '54'
doi: 10.1109/TASL.2008.925879
intvolume: '        16'
issue: '5'
keyword:
- automatic speech recognition
- bit errors
- codecs
- communication links
- corrupted observations
- decoding
- distributed speech recognition
- error-prone communication network
- feature vector sequence
- hidden Markov model-based ASR
- hidden Markov models
- inter-frame correlation
- Internet telephony
- network speech recognition
- packet loss
- speech posterior
- speech recognition
- transmission error robust speech recognition
- uncertainty decoding
- voice-over-IP codecs
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2008/IoHa08-1.pdf
oa: '1'
page: 1047-1060
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: A Novel Uncertainty Decoding Rule With Applications to Transmission Error Robust
  Speech Recognition
type: journal_article
user_id: '44006'
volume: 16
year: '2008'
...
---
_id: '11828'
abstract:
- lang: eng
  text: 'In this paper we present a comparison of the recently proposed Soft-Feature
    Distributed Speech Recognition (SFDSR) with the two evaluated candidate codecs
    for Speech Enabled Services over wireless networks: Adaptive Multirate Codec (AMR)
    and the ETSI Extended Advanced Front-End for Distributed Speech Recognition (XAFE).
    It is shown that SFDSR achieves the best recognition performance on a simulated
    GSM transmission, followed by XAFE and AMR.We also present some new results concerning
    SFDSR which demonstrate the versatility of the approach. Further, a simple method
    is introduced which considerably reduces the computational effort.'
author:
- first_name: Valentin
  full_name: Ion, Valentin
  last_name: Ion
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Ion V, Haeb-Umbach R. A Comparison of Soft-Feature Distributed Speech Recognition
    with Candidate Codecs for Speech Enabled Mobile Services. In: <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)</i>. Vol 1.
    ; 2005:333-336. doi:<a href="https://doi.org/10.1109/ICASSP.2005.1415118">10.1109/ICASSP.2005.1415118</a>'
  apa: Ion, V., &#38; Haeb-Umbach, R. (2005). A Comparison of Soft-Feature Distributed
    Speech Recognition with Candidate Codecs for Speech Enabled Mobile Services. In
    <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP
    2005)</i> (Vol. 1, pp. 333–336). <a href="https://doi.org/10.1109/ICASSP.2005.1415118">https://doi.org/10.1109/ICASSP.2005.1415118</a>
  bibtex: '@inproceedings{Ion_Haeb-Umbach_2005, title={A Comparison of Soft-Feature
    Distributed Speech Recognition with Candidate Codecs for Speech Enabled Mobile
    Services}, volume={1}, DOI={<a href="https://doi.org/10.1109/ICASSP.2005.1415118">10.1109/ICASSP.2005.1415118</a>},
    booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2005)}, author={Ion, Valentin and Haeb-Umbach, Reinhold}, year={2005},
    pages={333–336} }'
  chicago: Ion, Valentin, and Reinhold Haeb-Umbach. “A Comparison of Soft-Feature
    Distributed Speech Recognition with Candidate Codecs for Speech Enabled Mobile
    Services.” In <i>IEEE International Conference on Acoustics, Speech and Signal
    Processing (ICASSP 2005)</i>, 1:333–36, 2005. <a href="https://doi.org/10.1109/ICASSP.2005.1415118">https://doi.org/10.1109/ICASSP.2005.1415118</a>.
  ieee: V. Ion and R. Haeb-Umbach, “A Comparison of Soft-Feature Distributed Speech
    Recognition with Candidate Codecs for Speech Enabled Mobile Services,” in <i>IEEE
    International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)</i>,
    2005, vol. 1, pp. 333–336.
  mla: Ion, Valentin, and Reinhold Haeb-Umbach. “A Comparison of Soft-Feature Distributed
    Speech Recognition with Candidate Codecs for Speech Enabled Mobile Services.”
    <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP
    2005)</i>, vol. 1, 2005, pp. 333–36, doi:<a href="https://doi.org/10.1109/ICASSP.2005.1415118">10.1109/ICASSP.2005.1415118</a>.
  short: 'V. Ion, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2005), 2005, pp. 333–336.'
date_created: 2019-07-12T05:29:02Z
date_updated: 2022-01-06T06:51:10Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2005.1415118
intvolume: '         1'
keyword:
- adaptive codes
- adaptive multirate codec
- AMR
- distributed speech recognition
- ETSI
- extended advanced front-end
- recognition performance
- SFDSR
- simulated GSM transmission
- soft-feature distributed speech recognition
- speech codecs
- speech coding
- speech recognition
- variable rate codes
- XAFE
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2005/IoHa05-2.pdf
oa: '1'
page: 333-336
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2005)
status: public
title: A Comparison of Soft-Feature Distributed Speech Recognition with Candidate
  Codecs for Speech Enabled Mobile Services
type: conference
user_id: '44006'
volume: 1
year: '2005'
...