---
_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: '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: '11864'
abstract:
- lang: eng
  text: In this work, an observation model for the joint compensation of noise and
    reverberation in the logarithmic mel power spectral density domain is considered.
    It relates the features of the noisy reverberant speech to those of the non-reverberant
    speech and the noise. In contrast to enhancement of features only corrupted by
    reverberation (reverberant features), enhancement of noisy reverberant features
    requires a more sophisticated model for the error introduced by the proposed observation
    model. In a first consideration, it will be shown that this error is highly dependent
    on the instantaneous ratio of the power of reverberant speech to the power of
    the noise and, moreover, sensitive to the phase between reverberant speech and
    noise in the short-time discrete Fourier domain. Afterwards, a statistically motivated
    approach will be presented allowing for the model of the observation error to
    be inferred from the error model previously used for the reverberation only case.
    Finally, the developed observation error model will be utilized in a Bayesian
    feature enhancement scheme, leading to improvements in word accuracy on the AURORA5
    database.
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 Statistical Observation Model For
    Noisy Reverberant Speech Features and its Application to Robust ASR. In: <i>Signal
    Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference
    On</i>. ; 2012.'
  apa: Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2012). A Statistical Observation
    Model For Noisy Reverberant Speech Features and its Application to Robust ASR.
    In <i>Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International
    Conference on</i>.
  bibtex: '@inproceedings{Leutnant_Krueger_Haeb-Umbach_2012, title={A Statistical
    Observation Model For Noisy Reverberant Speech Features and its Application to
    Robust ASR}, booktitle={Signal Processing, Communications and Computing (ICSPCC),
    2012 IEEE International Conference on}, author={Leutnant, Volker and Krueger,
    Alexander and Haeb-Umbach, Reinhold}, year={2012} }'
  chicago: Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “A Statistical
    Observation Model For Noisy Reverberant Speech Features and Its Application to
    Robust ASR.” In <i>Signal Processing, Communications and Computing (ICSPCC), 2012
    IEEE International Conference On</i>, 2012.
  ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “A Statistical Observation Model
    For Noisy Reverberant Speech Features and its Application to Robust ASR,” in <i>Signal
    Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference
    on</i>, 2012.
  mla: Leutnant, Volker, et al. “A Statistical Observation Model For Noisy Reverberant
    Speech Features and Its Application to Robust ASR.” <i>Signal Processing, Communications
    and Computing (ICSPCC), 2012 IEEE International Conference On</i>, 2012.
  short: 'V. Leutnant, A. Krueger, R. Haeb-Umbach, in: Signal Processing, Communications
    and Computing (ICSPCC), 2012 IEEE International Conference On, 2012.'
date_created: 2019-07-12T05:29:44Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
keyword:
- Robust Automatic Speech Recognition
- Bayesian feature enhancement
- observation model for reverberant and noisy speech
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6335731
oa: '1'
publication: Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International
  Conference on
status: public
title: A Statistical Observation Model For Noisy Reverberant Speech Features and its
  Application to Robust ASR
type: conference
user_id: '44006'
year: '2012'
...
---
_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'
...
---
_id: '11937'
abstract:
- lang: eng
  text: In automatic speech recognition, hidden Markov models (HMMs) are commonly
    used for speech decoding, while switching linear dynamic models (SLDMs) can be
    employed for a preceding model-based speech feature enhancement. In this paper,
    these model types are combined in order to obtain a novel iterative speech feature
    enhancement and recognition architecture. It is shown that speech feature enhancement
    with SLDMs can be improved by feeding back information from the HMM to the enhancement
    stage. Two different feedback structures are derived. In the first, the posteriors
    of the HMM states are used to control the model probabilities of the SLDMs, while
    in the second they are employed to directly influence the estimate of the speech
    feature distribution. Both approaches lead to improvements in recognition accuracy
    both on the AURORA2 and AURORA4 databases compared to non-iterative speech feature
    enhancement with SLDMs. It is also shown that a combination with uncertainty decoding
    further enhances performance.
author:
- first_name: Stefan
  full_name: Windmann, Stefan
  last_name: Windmann
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Windmann S, Haeb-Umbach R. Approaches to Iterative Speech Feature Enhancement
    and Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>.
    2009;17(5):974-984. doi:<a href="https://doi.org/10.1109/TASL.2009.2014894">10.1109/TASL.2009.2014894</a>
  apa: Windmann, S., &#38; Haeb-Umbach, R. (2009). Approaches to Iterative Speech
    Feature Enhancement and Recognition. <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i>, <i>17</i>(5), 974–984. <a href="https://doi.org/10.1109/TASL.2009.2014894">https://doi.org/10.1109/TASL.2009.2014894</a>
  bibtex: '@article{Windmann_Haeb-Umbach_2009, title={Approaches to Iterative Speech
    Feature Enhancement and Recognition}, volume={17}, DOI={<a href="https://doi.org/10.1109/TASL.2009.2014894">10.1109/TASL.2009.2014894</a>},
    number={5}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
    author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2009}, pages={974–984}
    }'
  chicago: 'Windmann, Stefan, and Reinhold Haeb-Umbach. “Approaches to Iterative Speech
    Feature Enhancement and Recognition.” <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i> 17, no. 5 (2009): 974–84. <a href="https://doi.org/10.1109/TASL.2009.2014894">https://doi.org/10.1109/TASL.2009.2014894</a>.'
  ieee: S. Windmann and R. Haeb-Umbach, “Approaches to Iterative Speech Feature Enhancement
    and Recognition,” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>,
    vol. 17, no. 5, pp. 974–984, 2009.
  mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Approaches to Iterative Speech
    Feature Enhancement and Recognition.” <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i>, vol. 17, no. 5, 2009, pp. 974–84, doi:<a href="https://doi.org/10.1109/TASL.2009.2014894">10.1109/TASL.2009.2014894</a>.
  short: S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
    Processing 17 (2009) 974–984.
date_created: 2019-07-12T05:31:08Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/TASL.2009.2014894
intvolume: '        17'
issue: '5'
keyword:
- AURORA2 databases
- AURORA4 databases
- automatic speech recognition
- feedback structures
- hidden Markov models
- HMM
- iterative methods
- iterative speech feature enhancement
- model probabilities
- speech decoding
- speech enhancement
- speech feature distribution
- speech recognition
- switching linear dynamic models
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2009/WiHa09-1.pdf
oa: '1'
page: 974-984
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Approaches to Iterative Speech Feature Enhancement and Recognition
type: journal_article
user_id: '44006'
volume: 17
year: '2009'
...
---
_id: '11939'
abstract:
- lang: eng
  text: In this paper a switching linear dynamical model (SLDM) approach for speech
    feature enhancement is improved by employing more accurate models for the dynamics
    of speech and noise. The model of the clean speech feature trajectory is improved
    by augmenting the state vector to capture information derived from the delta features.
    Further a hidden noise state variable is introduced to obtain a more elaborated
    model for the noise dynamics. Approximate Bayesian inference in the SLDM is carried
    out by a bank of extended Kalman filters, whose outputs are combined according
    to the a posteriori probability of the individual state models. Experimental results
    on the AURORA2 database show improved recognition accuracy.
author:
- first_name: Stefan
  full_name: Windmann, Stefan
  last_name: Windmann
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Windmann S, Haeb-Umbach R. Modeling the dynamics of speech and noise for speech
    feature enhancement in ASR. In: <i>IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2008)</i>. ; 2008:4409-4412. doi:<a href="https://doi.org/10.1109/ICASSP.2008.4518633">10.1109/ICASSP.2008.4518633</a>'
  apa: Windmann, S., &#38; Haeb-Umbach, R. (2008). Modeling the dynamics of speech
    and noise for speech feature enhancement in ASR. In <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2008)</i> (pp. 4409–4412).
    <a href="https://doi.org/10.1109/ICASSP.2008.4518633">https://doi.org/10.1109/ICASSP.2008.4518633</a>
  bibtex: '@inproceedings{Windmann_Haeb-Umbach_2008, title={Modeling the dynamics
    of speech and noise for speech feature enhancement in ASR}, DOI={<a href="https://doi.org/10.1109/ICASSP.2008.4518633">10.1109/ICASSP.2008.4518633</a>},
    booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2008)}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2008},
    pages={4409–4412} }'
  chicago: Windmann, Stefan, and Reinhold Haeb-Umbach. “Modeling the Dynamics of Speech
    and Noise for Speech Feature Enhancement in ASR.” In <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2008)</i>, 4409–12, 2008. <a
    href="https://doi.org/10.1109/ICASSP.2008.4518633">https://doi.org/10.1109/ICASSP.2008.4518633</a>.
  ieee: S. Windmann and R. Haeb-Umbach, “Modeling the dynamics of speech and noise
    for speech feature enhancement in ASR,” in <i>IEEE International Conference on
    Acoustics, Speech and Signal Processing (ICASSP 2008)</i>, 2008, pp. 4409–4412.
  mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Modeling the Dynamics of Speech
    and Noise for Speech Feature Enhancement in ASR.” <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2008)</i>, 2008, pp. 4409–12,
    doi:<a href="https://doi.org/10.1109/ICASSP.2008.4518633">10.1109/ICASSP.2008.4518633</a>.
  short: 'S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2008), 2008, pp. 4409–4412.'
date_created: 2019-07-12T05:31:11Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2008.4518633
keyword:
- a posteriori probability
- AURORA2 database
- Bayesian inference
- Bayes methods
- channel bank filters
- extended Kalman filter banks
- hidden noise state variable
- Kalman filters
- noise dynamics
- speech enhancement
- speech feature enhancement
- speech feature trajectory
- switching linear dynamical model approach
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2008/WiHa08-1.pdf
oa: '1'
page: 4409-4412
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2008)
status: public
title: Modeling the dynamics of speech and noise for speech feature enhancement in
  ASR
type: conference
user_id: '44006'
year: '2008'
...