---
_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: '11869'
abstract:
- lang: eng
  text: Amongst several data driven approaches for designing filters for the time
    sequence of spectral parameters, the linear discriminant analysis (LDA) based
    method has been proposed for automatic speech recognition. Here we apply LDA-based
    filter design to cepstral features, which better match the inherent assumption
    of this method that feature vector components are uncorrelated. Extensive recognition
    experiments have been conducted both on the standard TIMIT phone recognition task
    and on a proprietary 130-words command word task under various adverse environmental
    conditions, including reverberant data with real-life room impulse responses and
    data processed by acoustic echo cancellation algorithms. Significant error rate
    reductions have been achieved when applying the novel long-range feature filters
    compared to standard approaches employing cepstral mean normalization and delta
    and delta-delta features, in particular when facing acoustic echo cancellation
    scenarios and room reverberation. For example, the phone accuracy on reverberated
    TIMIT data could be increased from 50.7\% to 56.0\%
author:
- first_name: M.
  full_name: Lieb, M.
  last_name: Lieb
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Lieb M, Haeb-Umbach R. LDA derived cepstral trajectory filters in adverse
    environmental conditions. In: <i>IEEE International Conference on Acoustics, Speech,
    and Signal Processing (ICASSP 2000)</i>. Vol 2. ; 2000:II1105-II1108 vol.2. doi:<a
    href="https://doi.org/10.1109/ICASSP.2000.859157">10.1109/ICASSP.2000.859157</a>'
  apa: Lieb, M., &#38; Haeb-Umbach, R. (2000). LDA derived cepstral trajectory filters
    in adverse environmental conditions. In <i>IEEE International Conference on Acoustics,
    Speech, and Signal Processing (ICASSP 2000)</i> (Vol. 2, pp. II1105-II1108 vol.2).
    <a href="https://doi.org/10.1109/ICASSP.2000.859157">https://doi.org/10.1109/ICASSP.2000.859157</a>
  bibtex: '@inproceedings{Lieb_Haeb-Umbach_2000, title={LDA derived cepstral trajectory
    filters in adverse environmental conditions}, volume={2}, DOI={<a href="https://doi.org/10.1109/ICASSP.2000.859157">10.1109/ICASSP.2000.859157</a>},
    booktitle={IEEE International Conference on Acoustics, Speech, and Signal Processing
    (ICASSP 2000)}, author={Lieb, M. and Haeb-Umbach, Reinhold}, year={2000}, pages={II1105-II1108
    vol.2} }'
  chicago: Lieb, M., and Reinhold Haeb-Umbach. “LDA Derived Cepstral Trajectory Filters
    in Adverse Environmental Conditions.” In <i>IEEE International Conference on Acoustics,
    Speech, and Signal Processing (ICASSP 2000)</i>, 2:II1105-II1108 vol.2, 2000.
    <a href="https://doi.org/10.1109/ICASSP.2000.859157">https://doi.org/10.1109/ICASSP.2000.859157</a>.
  ieee: M. Lieb and R. Haeb-Umbach, “LDA derived cepstral trajectory filters in adverse
    environmental conditions,” in <i>IEEE International Conference on Acoustics, Speech,
    and Signal Processing (ICASSP 2000)</i>, 2000, vol. 2, pp. II1105-II1108 vol.2.
  mla: Lieb, M., and Reinhold Haeb-Umbach. “LDA Derived Cepstral Trajectory Filters
    in Adverse Environmental Conditions.” <i>IEEE International Conference on Acoustics,
    Speech, and Signal Processing (ICASSP 2000)</i>, vol. 2, 2000, pp. II1105-II1108
    vol.2, doi:<a href="https://doi.org/10.1109/ICASSP.2000.859157">10.1109/ICASSP.2000.859157</a>.
  short: 'M. Lieb, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
    Speech, and Signal Processing (ICASSP 2000), 2000, pp. II1105-II1108 vol.2.'
date_created: 2019-07-12T05:29:50Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2000.859157
intvolume: '         2'
keyword:
- acoustic echo cancellation algorithms
- adverse environmental conditions
- automatic speech recognition
- cepstral analysis
- cepstral features
- cepstral mean normalization
- command word task
- delta-delta features
- delta features
- echo suppression
- error rate reductions
- feature vector components
- FIR filters
- LDA derived cepstral trajectory filters
- linear discriminant analysis
- long-range feature filters
- phone accuracy
- real-life room impulse responses
- reverberant data
- spectral parameters
- speech recognition
- standard TIMIT phone recognition task
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2000/LiHa00.pdf
oa: '1'
page: II1105-II1108 vol.2
publication: IEEE International Conference on Acoustics, Speech, and Signal Processing
  (ICASSP 2000)
status: public
title: LDA derived cepstral trajectory filters in adverse environmental conditions
type: conference
user_id: '44006'
volume: 2
year: '2000'
...