---
_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: '11740'
abstract:
- lang: eng
  text: In this contribution we derive the Maximum A-Posteriori (MAP) estimates of
    the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations.
    We assume the distortion to be white Gaussian noise of known mean and variance.
    An approximate conjugate prior of the GMM parameters is derived allowing for a
    computationally efficient implementation in a sequential estimation framework.
    Simulations on artificially generated data demonstrate the superiority of the
    proposed method compared to the Maximum Likelihood technique and to the ordinary
    MAP approach, whose estimates are corrected by the known statistics of the distortion
    in a straightforward manner.
author:
- first_name: Aleksej
  full_name: Chinaev, Aleksej
  last_name: Chinaev
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Chinaev A, Haeb-Umbach R. MAP-based Estimation of the Parameters of a Gaussian
    Mixture Model in the Presence of Noisy Observations. In: <i>38th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>. ; 2013:3352-3356.
    doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638279">10.1109/ICASSP.2013.6638279</a>'
  apa: Chinaev, A., &#38; Haeb-Umbach, R. (2013). MAP-based Estimation of the Parameters
    of a Gaussian Mixture Model in the Presence of Noisy Observations. In <i>38th
    International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>
    (pp. 3352–3356). <a href="https://doi.org/10.1109/ICASSP.2013.6638279">https://doi.org/10.1109/ICASSP.2013.6638279</a>
  bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2013, title={MAP-based Estimation of
    the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations},
    DOI={<a href="https://doi.org/10.1109/ICASSP.2013.6638279">10.1109/ICASSP.2013.6638279</a>},
    booktitle={38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2013},
    pages={3352–3356} }'
  chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the
    Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations.”
    In <i>38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)</i>, 3352–56, 2013. <a href="https://doi.org/10.1109/ICASSP.2013.6638279">https://doi.org/10.1109/ICASSP.2013.6638279</a>.
  ieee: A. Chinaev and R. Haeb-Umbach, “MAP-based Estimation of the Parameters of
    a Gaussian Mixture Model in the Presence of Noisy Observations,” in <i>38th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013,
    pp. 3352–3356.
  mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters
    of a Gaussian Mixture Model in the Presence of Noisy Observations.” <i>38th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013,
    pp. 3352–56, doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638279">10.1109/ICASSP.2013.6638279</a>.
  short: 'A. Chinaev, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2013), 2013, pp. 3352–3356.'
date_created: 2019-07-12T05:27:20Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638279
keyword:
- Gaussian noise
- maximum likelihood estimation
- parameter estimation
- GMM parameter
- Gaussian mixture model
- MAP estimation
- Map-based estimation
- maximum a-posteriori estimation
- maximum likelihood technique
- noisy observation
- sequential estimation framework
- white Gaussian noise
- Additive noise
- Gaussian mixture model
- Maximum likelihood estimation
- Noise measurement
- Gaussian mixture model
- Maximum a posteriori estimation
- Maximum likelihood estimation
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13.pdf
oa: '1'
page: 3352-3356
publication: 38th International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2013)
publication_identifier:
  issn:
  - 1520-6149
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13_Poster.pdf
status: public
title: MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence
  of Noisy Observations
type: conference
user_id: '44006'
year: '2013'
...