---
_id: '11862'
abstract:
- lang: eng
  text: In this contribution we extend a previously proposed Bayesian approach for
    the enhancement of reverberant logarithmic mel power spectral coefficients for
    robust automatic speech recognition to the additional compensation of background
    noise. A recently proposed observation model is employed whose time-variant observation
    error statistics are obtained as a side product of the inference of the a posteriori
    probability density function of the clean speech feature vectors. Further a reduction
    of the computational effort and the memory requirements are achieved by using
    a recursive formulation of the observation model. The performance of the proposed
    algorithms is first experimentally studied on a connected digits recognition task
    with artificially created noisy reverberant data. It is shown that the use of
    the time-variant observation error model leads to a significant error rate reduction
    at low signal-to-noise ratios compared to a time-invariant model. Further experiments
    were conducted on a 5000 word task recorded in a reverberant and noisy environment.
    A significant word error rate reduction was obtained demonstrating the effectiveness
    of the approach on real-world data.
author:
- first_name: Volker
  full_name: Leutnant, Volker
  last_name: Leutnant
- first_name: Alexander
  full_name: Krueger, Alexander
  last_name: Krueger
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Leutnant V, Krueger A, Haeb-Umbach R. Bayesian Feature Enhancement for Reverberation
    and Noise Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i>. 2013;21(8):1640-1652. doi:<a href="https://doi.org/10.1109/TASL.2013.2258013">10.1109/TASL.2013.2258013</a>
  apa: Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2013). Bayesian Feature Enhancement
    for Reverberation and Noise Robust Speech Recognition. <i>IEEE Transactions on
    Audio, Speech, and Language Processing</i>, <i>21</i>(8), 1640–1652. <a href="https://doi.org/10.1109/TASL.2013.2258013">https://doi.org/10.1109/TASL.2013.2258013</a>
  bibtex: '@article{Leutnant_Krueger_Haeb-Umbach_2013, title={Bayesian Feature Enhancement
    for Reverberation and Noise Robust Speech Recognition}, volume={21}, DOI={<a href="https://doi.org/10.1109/TASL.2013.2258013">10.1109/TASL.2013.2258013</a>},
    number={8}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
    author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2013},
    pages={1640–1652} }'
  chicago: 'Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “Bayesian
    Feature Enhancement for Reverberation and Noise Robust Speech Recognition.” <i>IEEE
    Transactions on Audio, Speech, and Language Processing</i> 21, no. 8 (2013): 1640–52.
    <a href="https://doi.org/10.1109/TASL.2013.2258013">https://doi.org/10.1109/TASL.2013.2258013</a>.'
  ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “Bayesian Feature Enhancement
    for Reverberation and Noise Robust Speech Recognition,” <i>IEEE Transactions on
    Audio, Speech, and Language Processing</i>, vol. 21, no. 8, pp. 1640–1652, 2013.
  mla: Leutnant, Volker, et al. “Bayesian Feature Enhancement for Reverberation and
    Noise Robust Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language
    Processing</i>, vol. 21, no. 8, 2013, pp. 1640–52, doi:<a href="https://doi.org/10.1109/TASL.2013.2258013">10.1109/TASL.2013.2258013</a>.
  short: V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech,
    and Language Processing 21 (2013) 1640–1652.
date_created: 2019-07-12T05:29:42Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2013.2258013
intvolume: '        21'
issue: '8'
keyword:
- Bayes methods
- compensation
- error statistics
- reverberation
- speech recognition
- Bayesian feature enhancement
- background noise
- clean speech feature vectors
- compensation
- connected digits recognition task
- error statistics
- memory requirements
- noisy reverberant data
- posteriori probability density function
- recursive formulation
- reverberant logarithmic mel power spectral coefficients
- robust automatic speech recognition
- signal-to-noise ratios
- time-variant observation
- word error rate reduction
- Robust automatic speech recognition
- model-based Bayesian feature enhancement
- observation model for reverberant and noisy speech
- recursive observation model
language:
- iso: eng
page: 1640-1652
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition
type: journal_article
user_id: '44006'
volume: 21
year: '2013'
...
---
_id: '11824'
abstract:
- lang: eng
  text: Soft-feature based speech recognition, which is an example of uncertainty
    decoding, has been proven to be a robust error mitigation method for distributed
    speech recognition over wireless channels exhibiting bit errors. In this paper
    we extend this concept to packet-oriented transmissions. The a posteriori probability
    density function of the lost feature vector, given the closest received neighbours,
    is computed. In the experiments, the nearest frame repetition, which is shown
    to be equivalent to the MAP estimate, outperforms the MMSE estimate for long bursts.
    Taking the variance into account at the speech recognition stage results in superior
    performance compared to classical schemes using point estimates. A computationally
    and memory efficient implementation of the proposed packet loss compensation scheme
    based on table lookup is presented
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. An Inexpensive Packet Loss Compensation Scheme for Distributed
    Speech Recognition Based on Soft-Features. In: <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>. Vol 1. ; 2006:I.
    doi:<a href="https://doi.org/10.1109/ICASSP.2006.1659984">10.1109/ICASSP.2006.1659984</a>'
  apa: Ion, V., &#38; Haeb-Umbach, R. (2006). An Inexpensive Packet Loss Compensation
    Scheme for Distributed Speech Recognition Based on Soft-Features. In <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i> (Vol.
    1, p. I). <a href="https://doi.org/10.1109/ICASSP.2006.1659984">https://doi.org/10.1109/ICASSP.2006.1659984</a>
  bibtex: '@inproceedings{Ion_Haeb-Umbach_2006, title={An Inexpensive Packet Loss
    Compensation Scheme for Distributed Speech Recognition Based on Soft-Features},
    volume={1}, DOI={<a href="https://doi.org/10.1109/ICASSP.2006.1659984">10.1109/ICASSP.2006.1659984</a>},
    booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2006)}, author={Ion, Valentin and Haeb-Umbach, Reinhold}, year={2006},
    pages={I} }'
  chicago: Ion, Valentin, and Reinhold Haeb-Umbach. “An Inexpensive Packet Loss Compensation
    Scheme for Distributed Speech Recognition Based on Soft-Features.” In <i>IEEE
    International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>,
    1:I, 2006. <a href="https://doi.org/10.1109/ICASSP.2006.1659984">https://doi.org/10.1109/ICASSP.2006.1659984</a>.
  ieee: V. Ion and R. Haeb-Umbach, “An Inexpensive Packet Loss Compensation Scheme
    for Distributed Speech Recognition Based on Soft-Features,” in <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, 2006,
    vol. 1, p. I.
  mla: Ion, Valentin, and Reinhold Haeb-Umbach. “An Inexpensive Packet Loss Compensation
    Scheme for Distributed Speech Recognition Based on Soft-Features.” <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, vol.
    1, 2006, p. I, doi:<a href="https://doi.org/10.1109/ICASSP.2006.1659984">10.1109/ICASSP.2006.1659984</a>.
  short: 'V. Ion, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2006), 2006, p. I.'
date_created: 2019-07-12T05:28:58Z
date_updated: 2022-01-06T06:51:10Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2006.1659984
intvolume: '         1'
keyword:
- distributed speech recognition
- least mean squares methods
- MAP estimate
- maximum likelihood estimation
- MMSE estimate
- packet loss compensation scheme
- packet switched communication
- posteriori probability density function
- robust error mitigation method
- soft-features
- speech recognition
- table lookup
- voice communication
- wireless channels
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2006/IoHa06-2.pdf
oa: '1'
page: I
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2006)
status: public
title: An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition
  Based on Soft-Features
type: conference
user_id: '44006'
volume: 1
year: '2006'
...