---
_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: '11861'
abstract:
- lang: eng
  text: 'In this contribution we present a theoretical and experimental investigation
    into the effects of reverberation and noise on features in the logarithmic mel
    power spectral domain, an intermediate stage in the computation of the mel frequency
    cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining
    insight into the complex interaction between clean speech, noise, and noisy reverberant
    speech features is essential for any ASR system to be robust against noise and
    reverberation present in distant microphone input signals. The findings are gathered
    in a probabilistic formulation of an observation model which may be used in model-based
    feature compensation schemes. The proposed observation model extends previous
    models in three major directions: First, the contribution of additive background
    noise to the observation error is explicitly taken into account. Second, an energy
    compensation constant is introduced which ensures an unbiased estimate of the
    reverberant speech features, and, third, a recursive variant of the observation
    model is developed resulting in reduced computational complexity when used in
    model-based feature compensation. The experimental section is used to evaluate
    the accuracy of the model and to describe how its parameters can be determined
    from test 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. A New Observation Model in the Logarithmic
    Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech.
    <i>IEEE/ACM Transactions on Audio, Speech, and Language Processing</i>. 2014;22(1):95-109.
    doi:<a href="https://doi.org/10.1109/TASLP.2013.2285480">10.1109/TASLP.2013.2285480</a>
  apa: Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2014). A New Observation
    Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition
    of Noisy Reverberant Speech. <i>IEEE/ACM Transactions on Audio, Speech, and Language
    Processing</i>, <i>22</i>(1), 95–109. <a href="https://doi.org/10.1109/TASLP.2013.2285480">https://doi.org/10.1109/TASLP.2013.2285480</a>
  bibtex: '@article{Leutnant_Krueger_Haeb-Umbach_2014, title={A New Observation Model
    in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of
    Noisy Reverberant Speech}, volume={22}, DOI={<a href="https://doi.org/10.1109/TASLP.2013.2285480">10.1109/TASLP.2013.2285480</a>},
    number={1}, journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
    author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2014},
    pages={95–109} }'
  chicago: 'Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “A New
    Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic
    Recognition of Noisy Reverberant Speech.” <i>IEEE/ACM Transactions on Audio, Speech,
    and Language Processing</i> 22, no. 1 (2014): 95–109. <a href="https://doi.org/10.1109/TASLP.2013.2285480">https://doi.org/10.1109/TASLP.2013.2285480</a>.'
  ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “A New Observation Model in the
    Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant
    Speech,” <i>IEEE/ACM Transactions on Audio, Speech, and Language Processing</i>,
    vol. 22, no. 1, pp. 95–109, 2014.
  mla: Leutnant, Volker, et al. “A New Observation Model in the Logarithmic Mel Power
    Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech.” <i>IEEE/ACM
    Transactions on Audio, Speech, and Language Processing</i>, vol. 22, no. 1, 2014,
    pp. 95–109, doi:<a href="https://doi.org/10.1109/TASLP.2013.2285480">10.1109/TASLP.2013.2285480</a>.
  short: V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE/ACM Transactions on Audio,
    Speech, and Language Processing 22 (2014) 95–109.
date_created: 2019-07-12T05:29:41Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASLP.2013.2285480
intvolume: '        22'
issue: '1'
keyword:
- computational complexity
- reverberation
- speech recognition
- automatic speech recognition
- background noise
- clean speech
- computational complexity
- energy compensation
- logarithmic mel power spectral domain
- mel frequency cepstral coefficients
- microphone input signals
- model-based feature compensation schemes
- noisy reverberant speech automatic recognition
- noisy reverberant speech features
- reverberation
- Atmospheric modeling
- Computational modeling
- Noise
- Noise measurement
- Reverberation
- Speech
- Vectors
- Model-based feature compensation
- observation model for reverberant and noisy speech
- recursive observation model
- robust automatic speech recognition
language:
- iso: eng
page: 95-109
publication: IEEE/ACM Transactions on Audio, Speech, and Language Processing
publication_identifier:
  issn:
  - 2329-9290
status: public
title: A New Observation Model in the Logarithmic Mel Power Spectral Domain for the
  Automatic Recognition of Noisy Reverberant Speech
type: journal_article
user_id: '44006'
volume: 22
year: '2014'
...
---
_id: '11716'
abstract:
- lang: eng
  text: The accuracy of automatic speech recognition systems in noisy and reverberant
    environments can be improved notably by exploiting the uncertainty of the estimated
    speech features using so-called uncertainty-of-observation techniques. In this
    paper, we introduce a new Bayesian decision rule that can serve as a mathematical
    framework from which both known and new uncertainty-of-observation techniques
    can be either derived or approximated. The new decision rule in its direct form
    leads to the new significance decoding approach for Gaussian mixture models, which
    results in better performance compared to standard uncertainty-of-observation
    techniques in different additive and convolutive noise scenarios.
author:
- first_name: Ahmed H.
  full_name: Abdelaziz, Ahmed H.
  last_name: Abdelaziz
- first_name: Steffen
  full_name: Zeiler, Steffen
  last_name: Zeiler
- first_name: Dorothea
  full_name: Kolossa, Dorothea
  last_name: Kolossa
- first_name: Volker
  full_name: Leutnant, Volker
  last_name: Leutnant
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Abdelaziz AH, Zeiler S, Kolossa D, Leutnant V, Haeb-Umbach R. GMM-based significance
    decoding. In: <i>Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
    Conference On</i>. ; 2013:6827-6831. doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638984">10.1109/ICASSP.2013.6638984</a>'
  apa: Abdelaziz, A. H., Zeiler, S., Kolossa, D., Leutnant, V., &#38; Haeb-Umbach,
    R. (2013). GMM-based significance decoding. In <i>Acoustics, Speech and Signal
    Processing (ICASSP), 2013 IEEE International Conference on</i> (pp. 6827–6831).
    <a href="https://doi.org/10.1109/ICASSP.2013.6638984">https://doi.org/10.1109/ICASSP.2013.6638984</a>
  bibtex: '@inproceedings{Abdelaziz_Zeiler_Kolossa_Leutnant_Haeb-Umbach_2013, title={GMM-based
    significance decoding}, DOI={<a href="https://doi.org/10.1109/ICASSP.2013.6638984">10.1109/ICASSP.2013.6638984</a>},
    booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
    Conference on}, author={Abdelaziz, Ahmed H. and Zeiler, Steffen and Kolossa, Dorothea
    and Leutnant, Volker and Haeb-Umbach, Reinhold}, year={2013}, pages={6827–6831}
    }'
  chicago: Abdelaziz, Ahmed H., Steffen Zeiler, Dorothea Kolossa, Volker Leutnant,
    and Reinhold Haeb-Umbach. “GMM-Based Significance Decoding.” In <i>Acoustics,
    Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On</i>,
    6827–31, 2013. <a href="https://doi.org/10.1109/ICASSP.2013.6638984">https://doi.org/10.1109/ICASSP.2013.6638984</a>.
  ieee: A. H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, and R. Haeb-Umbach, “GMM-based
    significance decoding,” in <i>Acoustics, Speech and Signal Processing (ICASSP),
    2013 IEEE International Conference on</i>, 2013, pp. 6827–6831.
  mla: Abdelaziz, Ahmed H., et al. “GMM-Based Significance Decoding.” <i>Acoustics,
    Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On</i>,
    2013, pp. 6827–31, doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638984">10.1109/ICASSP.2013.6638984</a>.
  short: 'A.H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, R. Haeb-Umbach, in:
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference
    On, 2013, pp. 6827–6831.'
date_created: 2019-07-12T05:26:53Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638984
keyword:
- Bayes methods
- Gaussian processes
- convolution
- decision theory
- decoding
- noise
- reverberation
- speech coding
- speech recognition
- Bayesian decision rule
- GMM
- Gaussian mixture models
- additive noise scenarios
- automatic speech recognition systems
- convolutive noise scenarios
- decoding approach
- mathematical framework
- reverberant environments
- significance decoding
- speech feature estimation
- uncertainty-of-observation techniques
- Hidden Markov models
- Maximum likelihood decoding
- Noise
- Speech
- Speech recognition
- Uncertainty
- Uncertainty-of-observation
- modified imputation
- noise robust speech recognition
- significance decoding
- uncertainty decoding
language:
- iso: eng
page: 6827-6831
publication: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
  Conference on
publication_identifier:
  issn:
  - 1520-6149
status: public
title: GMM-based significance decoding
type: conference
user_id: '44006'
year: '2013'
...
---
_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: '11850'
abstract:
- lang: eng
  text: In this paper, we present a novel blocking matrix and fixed beamformer design
    for a generalized sidelobe canceler for speech enhancement in a reverberant enclosure.
    They are based on a new method for estimating the acoustical transfer function
    ratios in the presence of stationary noise. The estimation method relies on solving
    a generalized eigenvalue problem in each frequency bin. An adaptive eigenvector
    tracking utilizing the power iteration method is employed and shown to achieve
    a high convergence speed. Simulation results demonstrate that the proposed beamformer
    leads to better noise and interference reduction and reduced speech distortions
    compared to other blocking matrix designs from the literature.
author:
- first_name: Alexander
  full_name: Krueger, Alexander
  last_name: Krueger
- first_name: Ernst
  full_name: Warsitz, Ernst
  last_name: Warsitz
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Krueger A, Warsitz E, Haeb-Umbach R. Speech Enhancement With a GSC-Like Structure
    Employing Eigenvector-Based Transfer Function Ratios Estimation. <i>IEEE Transactions
    on Audio, Speech, and Language Processing</i>. 2011;19(1):206-219. doi:<a href="https://doi.org/10.1109/TASL.2010.2047324">10.1109/TASL.2010.2047324</a>
  apa: Krueger, A., Warsitz, E., &#38; Haeb-Umbach, R. (2011). Speech Enhancement
    With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios
    Estimation. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>,
    <i>19</i>(1), 206–219. <a href="https://doi.org/10.1109/TASL.2010.2047324">https://doi.org/10.1109/TASL.2010.2047324</a>
  bibtex: '@article{Krueger_Warsitz_Haeb-Umbach_2011, title={Speech Enhancement With
    a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation},
    volume={19}, DOI={<a href="https://doi.org/10.1109/TASL.2010.2047324">10.1109/TASL.2010.2047324</a>},
    number={1}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
    author={Krueger, Alexander and Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2011},
    pages={206–219} }'
  chicago: 'Krueger, Alexander, Ernst Warsitz, and Reinhold Haeb-Umbach. “Speech Enhancement
    With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios
    Estimation.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>
    19, no. 1 (2011): 206–19. <a href="https://doi.org/10.1109/TASL.2010.2047324">https://doi.org/10.1109/TASL.2010.2047324</a>.'
  ieee: A. Krueger, E. Warsitz, and R. Haeb-Umbach, “Speech Enhancement With a GSC-Like
    Structure Employing Eigenvector-Based Transfer Function Ratios Estimation,” <i>IEEE
    Transactions on Audio, Speech, and Language Processing</i>, vol. 19, no. 1, pp.
    206–219, 2011.
  mla: Krueger, Alexander, et al. “Speech Enhancement With a GSC-Like Structure Employing
    Eigenvector-Based Transfer Function Ratios Estimation.” <i>IEEE Transactions on
    Audio, Speech, and Language Processing</i>, vol. 19, no. 1, 2011, pp. 206–19,
    doi:<a href="https://doi.org/10.1109/TASL.2010.2047324">10.1109/TASL.2010.2047324</a>.
  short: A. Krueger, E. Warsitz, R. Haeb-Umbach, IEEE Transactions on Audio, Speech,
    and Language Processing 19 (2011) 206–219.
date_created: 2019-07-12T05:29:28Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2010.2047324
intvolume: '        19'
issue: '1'
keyword:
- acoustical transfer function ratio
- adaptive eigenvector tracking
- array signal processing
- beamformer design
- blocking matrix
- eigenvalues and eigenfunctions
- eigenvector-based transfer function ratios estimation
- generalized sidelobe canceler
- interference reduction
- iterative methods
- power iteration method
- reduced speech distortions
- reverberant enclosure
- reverberation
- speech enhancement
- stationary noise
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2011/KrWaHa11.pdf
oa: '1'
page: 206-219
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer
  Function Ratios Estimation
type: journal_article
user_id: '44006'
volume: 19
year: '2011'
...
---
_id: '11846'
abstract:
- lang: eng
  text: In this paper, we present a new technique for automatic speech recognition
    (ASR) in reverberant environments. Our approach is aimed at the enhancement of
    the logarithmic Mel power spectrum, which is computed at an intermediate stage
    to obtain the widely used Mel frequency cepstral coefficients (MFCCs). Given the
    reverberant logarithmic Mel power spectral coefficients (LMPSCs), a minimum mean
    square error estimate of the clean LMPSCs is computed by carrying out Bayesian
    inference. We employ switching linear dynamical models as an a priori model for
    the dynamics of the clean LMPSCs. Further, we derive a stochastic observation
    model which relates the clean to the reverberant LMPSCs through a simplified model
    of the room impulse response (RIR). This model requires only two parameters, namely
    RIR energy and reverberation time, which can be estimated from the captured microphone
    signal. The performance of the proposed enhancement technique is studied on the
    AURORA5 database and compared to that of constrained maximum-likelihood linear
    regression (CMLLR). It is shown by experimental results that our approach significantly
    outperforms CMLLR and that up to 80\% of the errors caused by the reverberation
    are recovered. In addition to the fact that the approach is compatible with the
    standard MFCC feature vectors, it leaves the ASR back-end unchanged. It is of
    moderate computational complexity and suitable for real time applications.
author:
- 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: Krueger A, Haeb-Umbach R. Model-Based Feature Enhancement for Reverberant Speech
    Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>.
    2010;18(7):1692-1707. doi:<a href="https://doi.org/10.1109/TASL.2010.2049684">10.1109/TASL.2010.2049684</a>
  apa: Krueger, A., &#38; Haeb-Umbach, R. (2010). Model-Based Feature Enhancement
    for Reverberant Speech Recognition. <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i>, <i>18</i>(7), 1692–1707. <a href="https://doi.org/10.1109/TASL.2010.2049684">https://doi.org/10.1109/TASL.2010.2049684</a>
  bibtex: '@article{Krueger_Haeb-Umbach_2010, title={Model-Based Feature Enhancement
    for Reverberant Speech Recognition}, volume={18}, DOI={<a href="https://doi.org/10.1109/TASL.2010.2049684">10.1109/TASL.2010.2049684</a>},
    number={7}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
    author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2010}, pages={1692–1707}
    }'
  chicago: 'Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement
    for Reverberant Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i> 18, no. 7 (2010): 1692–1707. <a href="https://doi.org/10.1109/TASL.2010.2049684">https://doi.org/10.1109/TASL.2010.2049684</a>.'
  ieee: A. Krueger and R. Haeb-Umbach, “Model-Based Feature Enhancement for Reverberant
    Speech Recognition,” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>,
    vol. 18, no. 7, pp. 1692–1707, 2010.
  mla: Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement
    for Reverberant Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i>, vol. 18, no. 7, 2010, pp. 1692–707, doi:<a href="https://doi.org/10.1109/TASL.2010.2049684">10.1109/TASL.2010.2049684</a>.
  short: A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
    Processing 18 (2010) 1692–1707.
date_created: 2019-07-12T05:29:23Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2010.2049684
intvolume: '        18'
issue: '7'
keyword:
- ASR
- AURORA5 database
- automatic speech recognition
- Bayesian inference
- belief networks
- CMLLR
- computational complexity
- constrained maximum likelihood linear regression
- least mean squares methods
- LMPSC computation
- logarithmic Mel power spectrum
- maximum likelihood estimation
- Mel frequency cepstral coefficients
- MFCC feature vectors
- microphone signal
- minimum mean square error estimation
- model-based feature enhancement
- regression analysis
- reverberant speech recognition
- reverberation
- RIR energy
- room impulse response
- speech recognition
- stochastic observation model
- stochastic processes
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2010/KrHa10.pdf
oa: '1'
page: 1692-1707
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Model-Based Feature Enhancement for Reverberant Speech Recognition
type: journal_article
user_id: '44006'
volume: 18
year: '2010'
...