---
_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: '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: '11938'
abstract:
- lang: eng
  text: In this paper, parameter estimation of a state-space model of noise or noisy
    speech cepstra is investigated. A blockwise EM algorithm is derived for the estimation
    of the state and observation noise covariance from noise-only input data. It is
    supposed to be used during the offline training mode of a speech recognizer. Further
    a sequential online EM algorithm is developed to adapt the observation noise covariance
    on noisy speech cepstra at its input. The estimated parameters are then used in
    model-based speech feature enhancement for noise-robust automatic speech recognition.
    Experiments on the AURORA4 database lead to improved recognition results with
    a linear state model compared to the assumption of stationary noise.
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. Parameter Estimation of a State-Space Model of Noise
    for Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech, and Language
    Processing</i>. 2009;17(8):1577-1590. doi:<a href="https://doi.org/10.1109/TASL.2009.2023172">10.1109/TASL.2009.2023172</a>
  apa: Windmann, S., &#38; Haeb-Umbach, R. (2009). Parameter Estimation of a State-Space
    Model of Noise for Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech,
    and Language Processing</i>, <i>17</i>(8), 1577–1590. <a href="https://doi.org/10.1109/TASL.2009.2023172">https://doi.org/10.1109/TASL.2009.2023172</a>
  bibtex: '@article{Windmann_Haeb-Umbach_2009, title={Parameter Estimation of a State-Space
    Model of Noise for Robust Speech Recognition}, volume={17}, DOI={<a href="https://doi.org/10.1109/TASL.2009.2023172">10.1109/TASL.2009.2023172</a>},
    number={8}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
    author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2009}, pages={1577–1590}
    }'
  chicago: 'Windmann, Stefan, and Reinhold Haeb-Umbach. “Parameter Estimation of a
    State-Space Model of Noise for Robust Speech Recognition.” <i>IEEE Transactions
    on Audio, Speech, and Language Processing</i> 17, no. 8 (2009): 1577–90. <a href="https://doi.org/10.1109/TASL.2009.2023172">https://doi.org/10.1109/TASL.2009.2023172</a>.'
  ieee: S. Windmann and R. Haeb-Umbach, “Parameter Estimation of a State-Space Model
    of Noise for Robust Speech Recognition,” <i>IEEE Transactions on Audio, Speech,
    and Language Processing</i>, vol. 17, no. 8, pp. 1577–1590, 2009.
  mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Parameter Estimation of a State-Space
    Model of Noise for Robust Speech Recognition.” <i>IEEE Transactions on Audio,
    Speech, and Language Processing</i>, vol. 17, no. 8, 2009, pp. 1577–90, doi:<a
    href="https://doi.org/10.1109/TASL.2009.2023172">10.1109/TASL.2009.2023172</a>.
  short: S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
    Processing 17 (2009) 1577–1590.
date_created: 2019-07-12T05:31:09Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/TASL.2009.2023172
intvolume: '        17'
issue: '8'
keyword:
- AURORA4 database
- blockwise EM algorithm
- covariance analysis
- linear state model
- noise covariance
- noise-robust automatic speech recognition
- noisy speech cepstra
- offline training mode
- parameter estimation
- speech recognition
- speech recognition equipment
- speech recognizer
- state-space methods
- state-space model
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2009/WiHa09-2.pdf
oa: '1'
page: 1577-1590
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition
type: journal_article
user_id: '44006'
volume: 17
year: '2009'
...