---
_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'
...
---
_id: '11816'
abstract:
- lang: eng
  text: In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters
    of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the
    resulting Expectation Maximization (EM) algorithm delivers virtually biasfree
    and efficient estimates, and we discuss its convergence properties. We also discuss
    optimal classification in the presence of censored data. Censored data are frequently
    encountered in wireless LAN positioning systems based on the fingerprinting method
    employing signal strength measurements, due to the limited sensitivity of the
    portable devices. Experiments both on simulated and real-world data demonstrate
    the effectiveness of the proposed algorithms.
author:
- first_name: Manh Kha
  full_name: Hoang, Manh Kha
  last_name: Hoang
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Hoang MK, Haeb-Umbach R. Parameter estimation and classification of censored
    Gaussian data with application to WiFi indoor positioning. In: <i>38th International
    Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>. ; 2013:3721-3725.
    doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638353">10.1109/ICASSP.2013.6638353</a>'
  apa: Hoang, M. K., &#38; Haeb-Umbach, R. (2013). Parameter estimation and classification
    of censored Gaussian data with application to WiFi indoor positioning. In <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>
    (pp. 3721–3725). <a href="https://doi.org/10.1109/ICASSP.2013.6638353">https://doi.org/10.1109/ICASSP.2013.6638353</a>
  bibtex: '@inproceedings{Hoang_Haeb-Umbach_2013, title={Parameter estimation and
    classification of censored Gaussian data with application to WiFi indoor positioning},
    DOI={<a href="https://doi.org/10.1109/ICASSP.2013.6638353">10.1109/ICASSP.2013.6638353</a>},
    booktitle={38th International Conference on Acoustics, Speech, and Signal Processing
    (ICASSP 2013)}, author={Hoang, Manh Kha and Haeb-Umbach, Reinhold}, year={2013},
    pages={3721–3725} }'
  chicago: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
    of Censored Gaussian Data with Application to WiFi Indoor Positioning.” In <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>,
    3721–25, 2013. <a href="https://doi.org/10.1109/ICASSP.2013.6638353">https://doi.org/10.1109/ICASSP.2013.6638353</a>.
  ieee: M. K. Hoang and R. Haeb-Umbach, “Parameter estimation and classification of
    censored Gaussian data with application to WiFi indoor positioning,” in <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>,
    2013, pp. 3721–3725.
  mla: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
    of Censored Gaussian Data with Application to WiFi Indoor Positioning.” <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>,
    2013, pp. 3721–25, doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638353">10.1109/ICASSP.2013.6638353</a>.
  short: 'M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
    Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.'
date_created: 2019-07-12T05:28:48Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638353
keyword:
- Gaussian processes
- Global Positioning System
- convergence
- expectation-maximisation algorithm
- fingerprint identification
- indoor radio
- signal classification
- wireless LAN
- EM algorithm
- ML estimation
- WiFi indoor positioning
- censored Gaussian data classification
- clipped data
- convergence properties
- expectation maximization algorithm
- fingerprinting method
- maximum likelihood estimation
- optimal classification
- parameters estimation
- portable devices sensitivity
- signal strength measurements
- wireless LAN positioning systems
- Convergence
- IEEE 802.11 Standards
- Maximum likelihood estimation
- Parameter estimation
- Position measurement
- Training
- Indoor positioning
- censored data
- expectation maximization
- signal strength
- wireless LAN
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf
oa: '1'
page: 3721-3725
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/HoHa2013_Poster.pdf
status: public
title: Parameter estimation and classification of censored Gaussian data with application
  to WiFi indoor positioning
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11845'
abstract:
- lang: eng
  text: The paper proposes a modification of the standard maximum a posteriori (MAP)
    method for the estimation of the parameters of a Gaussian process for cases where
    the process is superposed by additive Gaussian observation errors of known variance.
    Simulations on artificially generated data demonstrate the superiority of the
    proposed method. While reducing to the ordinary MAP approach in the absence of
    observation noise, the improvement becomes the more pronounced the larger the
    variance of the observation noise. The method is further extended to track the
    parameters in case of non-stationary Gaussian processes.
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. MAP-based estimation of the parameters of non-stationary
    Gaussian processes from noisy observations. In: <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2011)</i>. ; 2011:3596-3599.
    doi:<a href="https://doi.org/10.1109/ICASSP.2011.5946256">10.1109/ICASSP.2011.5946256</a>'
  apa: Krueger, A., &#38; Haeb-Umbach, R. (2011). MAP-based estimation of the parameters
    of non-stationary Gaussian processes from noisy observations. In <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)</i> (pp. 3596–3599).
    <a href="https://doi.org/10.1109/ICASSP.2011.5946256">https://doi.org/10.1109/ICASSP.2011.5946256</a>
  bibtex: '@inproceedings{Krueger_Haeb-Umbach_2011, title={MAP-based estimation of
    the parameters of non-stationary Gaussian processes from noisy observations},
    DOI={<a href="https://doi.org/10.1109/ICASSP.2011.5946256">10.1109/ICASSP.2011.5946256</a>},
    booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2011)}, author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2011},
    pages={3596–3599} }'
  chicago: Krueger, Alexander, and Reinhold Haeb-Umbach. “MAP-Based Estimation of
    the Parameters of Non-Stationary Gaussian Processes from Noisy Observations.”
    In <i>IEEE International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2011)</i>, 3596–99, 2011. <a href="https://doi.org/10.1109/ICASSP.2011.5946256">https://doi.org/10.1109/ICASSP.2011.5946256</a>.
  ieee: A. Krueger and R. Haeb-Umbach, “MAP-based estimation of the parameters of
    non-stationary Gaussian processes from noisy observations,” in <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)</i>, 2011,
    pp. 3596–3599.
  mla: Krueger, Alexander, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the
    Parameters of Non-Stationary Gaussian Processes from Noisy Observations.” <i>IEEE
    International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)</i>,
    2011, pp. 3596–99, doi:<a href="https://doi.org/10.1109/ICASSP.2011.5946256">10.1109/ICASSP.2011.5946256</a>.
  short: 'A. Krueger, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2011), 2011, pp. 3596–3599.'
date_created: 2019-07-12T05:29:22Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2011.5946256
keyword:
- Gaussian processes
- MAP-based estimation
- maximum a posteriori method
- maximum likelihood estimation
- nonstationary Gaussian processes
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2011/KrHa11.pdf
oa: '1'
page: 3596-3599
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2011)
status: public
title: MAP-based estimation of the parameters of non-stationary Gaussian processes
  from noisy observations
type: conference
user_id: '44006'
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'
...
---
_id: '11785'
abstract:
- lang: eng
  text: 'In this paper we present a novel channel impulse response estimation technique
    for block-oriented OFDM transmission based on combining estimators: the estimates
    provided by a Kalman filter operating in the time domain and a Wiener filter in
    the frequency domain are optimally combined by taking into account their estimated
    error covariances. The resulting estimator turns out to be identical to the MAP
    estimator of correlated jointly Gaussian mean vectors. Different variants of the
    proposed scheme are experimentally investigated in an EEEE 802.11a-like system
    setup. They compare favourably with known approaches from the literature resulting
    in reduced mean square estimation error and bit error rate. Further, robustness
    and complexity issues are discussed'
author:
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
- first_name: Maik
  full_name: Bevermeier, Maik
  last_name: Bevermeier
citation:
  ama: 'Haeb-Umbach R, Bevermeier M. OFDM Channel Estimation Based on Combined Estimation
    in Time and Frequency Domain. In: <i>IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2007)</i>. Vol 3. ; 2007:III-277-III-280.
    doi:<a href="https://doi.org/10.1109/ICASSP.2007.366526">10.1109/ICASSP.2007.366526</a>'
  apa: Haeb-Umbach, R., &#38; Bevermeier, M. (2007). OFDM Channel Estimation Based
    on Combined Estimation in Time and Frequency Domain. In <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i> (Vol.
    3, pp. III-277-III–280). <a href="https://doi.org/10.1109/ICASSP.2007.366526">https://doi.org/10.1109/ICASSP.2007.366526</a>
  bibtex: '@inproceedings{Haeb-Umbach_Bevermeier_2007, title={OFDM Channel Estimation
    Based on Combined Estimation in Time and Frequency Domain}, volume={3}, DOI={<a
    href="https://doi.org/10.1109/ICASSP.2007.366526">10.1109/ICASSP.2007.366526</a>},
    booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2007)}, author={Haeb-Umbach, Reinhold and Bevermeier, Maik}, year={2007},
    pages={III-277-III–280} }'
  chicago: Haeb-Umbach, Reinhold, and Maik Bevermeier. “OFDM Channel Estimation Based
    on Combined Estimation in Time and Frequency Domain.” In <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>, 3:III-277-III–280,
    2007. <a href="https://doi.org/10.1109/ICASSP.2007.366526">https://doi.org/10.1109/ICASSP.2007.366526</a>.
  ieee: R. Haeb-Umbach and M. Bevermeier, “OFDM Channel Estimation Based on Combined
    Estimation in Time and Frequency Domain,” in <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>, 2007, vol. 3, pp.
    III-277-III–280.
  mla: Haeb-Umbach, Reinhold, and Maik Bevermeier. “OFDM Channel Estimation Based
    on Combined Estimation in Time and Frequency Domain.” <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>, vol. 3, 2007, pp.
    III-277-III–280, doi:<a href="https://doi.org/10.1109/ICASSP.2007.366526">10.1109/ICASSP.2007.366526</a>.
  short: 'R. Haeb-Umbach, M. Bevermeier, in: IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2007), 2007, pp. III-277-III–280.'
date_created: 2019-07-12T05:28:13Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2007.366526
intvolume: '         3'
keyword:
- bit error rate
- block-oriented OFDM transmission
- channel estimation
- channel impulse response estimation
- combining estimators
- error statistics
- frequency domain estimation
- Gaussian mean vectors
- Gaussian processes
- Kalman filter
- Kalman filters
- MAP estimator
- maximum likelihood estimation
- OFDM channel estimation
- OFDM modulation
- time domain estimation
- time-frequency analysis
- Wiener filter
- Wiener filters
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2007/HaBe07.pdf
oa: '1'
page: III-277-III-280
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2007)
status: public
title: OFDM Channel Estimation Based on Combined Estimation in Time and Frequency
  Domain
type: conference
user_id: '44006'
volume: 3
year: '2007'
...
---
_id: '11824'
abstract:
- lang: eng
  text: Soft-feature based speech recognition, which is an example of uncertainty
    decoding, has been proven to be a robust error mitigation method for distributed
    speech recognition over wireless channels exhibiting bit errors. In this paper
    we extend this concept to packet-oriented transmissions. The a posteriori probability
    density function of the lost feature vector, given the closest received neighbours,
    is computed. In the experiments, the nearest frame repetition, which is shown
    to be equivalent to the MAP estimate, outperforms the MMSE estimate for long bursts.
    Taking the variance into account at the speech recognition stage results in superior
    performance compared to classical schemes using point estimates. A computationally
    and memory efficient implementation of the proposed packet loss compensation scheme
    based on table lookup is presented
author:
- first_name: Valentin
  full_name: Ion, Valentin
  last_name: Ion
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Ion V, Haeb-Umbach R. An Inexpensive Packet Loss Compensation Scheme for Distributed
    Speech Recognition Based on Soft-Features. In: <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>. Vol 1. ; 2006:I.
    doi:<a href="https://doi.org/10.1109/ICASSP.2006.1659984">10.1109/ICASSP.2006.1659984</a>'
  apa: Ion, V., &#38; Haeb-Umbach, R. (2006). An Inexpensive Packet Loss Compensation
    Scheme for Distributed Speech Recognition Based on Soft-Features. In <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i> (Vol.
    1, p. I). <a href="https://doi.org/10.1109/ICASSP.2006.1659984">https://doi.org/10.1109/ICASSP.2006.1659984</a>
  bibtex: '@inproceedings{Ion_Haeb-Umbach_2006, title={An Inexpensive Packet Loss
    Compensation Scheme for Distributed Speech Recognition Based on Soft-Features},
    volume={1}, DOI={<a href="https://doi.org/10.1109/ICASSP.2006.1659984">10.1109/ICASSP.2006.1659984</a>},
    booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2006)}, author={Ion, Valentin and Haeb-Umbach, Reinhold}, year={2006},
    pages={I} }'
  chicago: Ion, Valentin, and Reinhold Haeb-Umbach. “An Inexpensive Packet Loss Compensation
    Scheme for Distributed Speech Recognition Based on Soft-Features.” In <i>IEEE
    International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>,
    1:I, 2006. <a href="https://doi.org/10.1109/ICASSP.2006.1659984">https://doi.org/10.1109/ICASSP.2006.1659984</a>.
  ieee: V. Ion and R. Haeb-Umbach, “An Inexpensive Packet Loss Compensation Scheme
    for Distributed Speech Recognition Based on Soft-Features,” in <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, 2006,
    vol. 1, p. I.
  mla: Ion, Valentin, and Reinhold Haeb-Umbach. “An Inexpensive Packet Loss Compensation
    Scheme for Distributed Speech Recognition Based on Soft-Features.” <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, vol.
    1, 2006, p. I, doi:<a href="https://doi.org/10.1109/ICASSP.2006.1659984">10.1109/ICASSP.2006.1659984</a>.
  short: 'V. Ion, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2006), 2006, p. I.'
date_created: 2019-07-12T05:28:58Z
date_updated: 2022-01-06T06:51:10Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2006.1659984
intvolume: '         1'
keyword:
- distributed speech recognition
- least mean squares methods
- MAP estimate
- maximum likelihood estimation
- MMSE estimate
- packet loss compensation scheme
- packet switched communication
- posteriori probability density function
- robust error mitigation method
- soft-features
- speech recognition
- table lookup
- voice communication
- wireless channels
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2006/IoHa06-2.pdf
oa: '1'
page: I
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2006)
status: public
title: An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition
  Based on Soft-Features
type: conference
user_id: '44006'
volume: 1
year: '2006'
...
---
_id: '11778'
abstract:
- lang: eng
  text: In this paper, it is shown that a correlation criterion is the appropriate
    criterion for bottom-up clustering to obtain broad phonetic class regression trees
    for maximum likelihood linear regression (MLLR)-based speaker adaptation. The
    correlation structure among speech units is estimated on the speaker-independent
    training data. In adaptation experiments the tree outperformed a regression tree
    obtained from clustering according to closeness in acoustic space and achieved
    results comparable with those of a manually designed broad phonetic class tree
author:
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Haeb-Umbach R. Automatic generation of phonetic regression class trees for
    MLLR adaptation. <i>IEEE Transactions on Speech and Audio Processing</i>. 2001;9(3):299-302.
    doi:<a href="https://doi.org/10.1109/89.906003">10.1109/89.906003</a>
  apa: Haeb-Umbach, R. (2001). Automatic generation of phonetic regression class trees
    for MLLR adaptation. <i>IEEE Transactions on Speech and Audio Processing</i>,
    <i>9</i>(3), 299–302. <a href="https://doi.org/10.1109/89.906003">https://doi.org/10.1109/89.906003</a>
  bibtex: '@article{Haeb-Umbach_2001, title={Automatic generation of phonetic regression
    class trees for MLLR adaptation}, volume={9}, DOI={<a href="https://doi.org/10.1109/89.906003">10.1109/89.906003</a>},
    number={3}, journal={IEEE Transactions on Speech and Audio Processing}, author={Haeb-Umbach,
    Reinhold}, year={2001}, pages={299–302} }'
  chicago: 'Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class
    Trees for MLLR Adaptation.” <i>IEEE Transactions on Speech and Audio Processing</i>
    9, no. 3 (2001): 299–302. <a href="https://doi.org/10.1109/89.906003">https://doi.org/10.1109/89.906003</a>.'
  ieee: R. Haeb-Umbach, “Automatic generation of phonetic regression class trees for
    MLLR adaptation,” <i>IEEE Transactions on Speech and Audio Processing</i>, vol.
    9, no. 3, pp. 299–302, 2001.
  mla: Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class Trees
    for MLLR Adaptation.” <i>IEEE Transactions on Speech and Audio Processing</i>,
    vol. 9, no. 3, 2001, pp. 299–302, doi:<a href="https://doi.org/10.1109/89.906003">10.1109/89.906003</a>.
  short: R. Haeb-Umbach, IEEE Transactions on Speech and Audio Processing 9 (2001)
    299–302.
date_created: 2019-07-12T05:28:04Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/89.906003
intvolume: '         9'
issue: '3'
keyword:
- acoustic space
- adaptation experiments
- automatic generation
- bottom-up clustering
- broad phonetic class regression trees
- correlation criterion
- correlation methods
- maximum likelihood estimation
- maximum likelihood linear regression based speaker adaptation
- MLLR adaptation
- pattern clustering
- phonetic regression class trees
- speaker-independent training data
- speech recognition
- speech units
- statistical analysis
- trees (mathematics)
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2001/Ha01.pdf
oa: '1'
page: 299-302
publication: IEEE Transactions on Speech and Audio Processing
status: public
title: Automatic generation of phonetic regression class trees for MLLR adaptation
type: journal_article
user_id: '44006'
volume: 9
year: '2001'
...