---
_id: '11917'
abstract:
- lang: eng
  text: In this paper we present a speech presence probability (SPP) estimation algorithmwhich
    exploits both temporal and spectral correlations of speech. To this end, the SPP
    estimation is formulated as the posterior probability estimation of the states
    of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm
    to decode the 2D-HMM which is based on the turbo principle. The experimental results
    show that indeed the SPP estimates improve from iteration to iteration, and further
    clearly outperform another state-of-the-art SPP estimation algorithm.
author:
- first_name: Dang Hai Tran
  full_name: Vu, Dang Hai Tran
  last_name: Vu
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Vu DHT, Haeb-Umbach R. Using the turbo principle for exploiting temporal and
    spectral correlations in speech presence probability estimation. In: <i>38th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>. ; 2013:863-867.
    doi:<a href="https://doi.org/10.1109/ICASSP.2013.6637771">10.1109/ICASSP.2013.6637771</a>'
  apa: Vu, D. H. T., &#38; Haeb-Umbach, R. (2013). Using the turbo principle for exploiting
    temporal and spectral correlations in speech presence probability estimation.
    In <i>38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)</i> (pp. 863–867). <a href="https://doi.org/10.1109/ICASSP.2013.6637771">https://doi.org/10.1109/ICASSP.2013.6637771</a>
  bibtex: '@inproceedings{Vu_Haeb-Umbach_2013, title={Using the turbo principle for
    exploiting temporal and spectral correlations in speech presence probability estimation},
    DOI={<a href="https://doi.org/10.1109/ICASSP.2013.6637771">10.1109/ICASSP.2013.6637771</a>},
    booktitle={38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)}, author={Vu, Dang Hai Tran and Haeb-Umbach, Reinhold}, year={2013},
    pages={863–867} }'
  chicago: Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle
    for Exploiting Temporal and Spectral Correlations in Speech Presence Probability
    Estimation.” In <i>38th International Conference on Acoustics, Speech and Signal
    Processing (ICASSP 2013)</i>, 863–67, 2013. <a href="https://doi.org/10.1109/ICASSP.2013.6637771">https://doi.org/10.1109/ICASSP.2013.6637771</a>.
  ieee: D. H. T. Vu and R. Haeb-Umbach, “Using the turbo principle for exploiting
    temporal and spectral correlations in speech presence probability estimation,”
    in <i>38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)</i>, 2013, pp. 863–867.
  mla: Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for
    Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.”
    <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP
    2013)</i>, 2013, pp. 863–67, doi:<a href="https://doi.org/10.1109/ICASSP.2013.6637771">10.1109/ICASSP.2013.6637771</a>.
  short: 'D.H.T. Vu, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2013), 2013, pp. 863–867.'
date_created: 2019-07-12T05:30:45Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6637771
keyword:
- correlation methods
- estimation theory
- hidden Markov models
- iterative methods
- probability
- spectral analysis
- speech processing
- 2D HMM
- SPP estimates
- iterative algorithm
- posterior probability estimation
- spectral correlation
- speech presence probability estimation
- state-of-the-art SPP estimation algorithm
- temporal correlation
- turbo principle
- two-dimensional hidden Markov model
- Correlation
- Decoding
- Estimation
- Iterative decoding
- Noise
- Speech
- Vectors
language:
- iso: eng
page: 863-867
publication: 38th International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2013)
publication_identifier:
  issn:
  - 1520-6149
status: public
title: Using the turbo principle for exploiting temporal and spectral correlations
  in speech presence probability estimation
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11820'
abstract:
- lang: eng
  text: In this paper, we derive an uncertainty decoding rule for automatic speech
    recognition (ASR), which accounts for both corrupted observations and inter-frame
    correlation. The conditional independence assumption, prevalent in hidden Markov
    model-based ASR, is relaxed to obtain a clean speech posterior that is conditioned
    on the complete observed feature vector sequence. This is a more informative posterior
    than one conditioned only on the current observation. The novel decoding is used
    to obtain a transmission-error robust remote ASR system, where the speech capturing
    unit is connected to the decoder via an error-prone communication network. We
    show how the clean speech posterior can be computed for communication links being
    characterized by either bit errors or packet loss. Recognition results are presented
    for both distributed and network speech recognition, where in the latter case
    common voice-over-IP codecs are employed.
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. A Novel Uncertainty Decoding Rule With Applications to
    Transmission Error Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech,
    and Language Processing</i>. 2008;16(5):1047-1060. doi:<a href="https://doi.org/10.1109/TASL.2008.925879">10.1109/TASL.2008.925879</a>
  apa: Ion, V., &#38; Haeb-Umbach, R. (2008). A Novel Uncertainty Decoding Rule With
    Applications to Transmission Error Robust Speech Recognition. <i>IEEE Transactions
    on Audio, Speech, and Language Processing</i>, <i>16</i>(5), 1047–1060. <a href="https://doi.org/10.1109/TASL.2008.925879">https://doi.org/10.1109/TASL.2008.925879</a>
  bibtex: '@article{Ion_Haeb-Umbach_2008, title={A Novel Uncertainty Decoding Rule
    With Applications to Transmission Error Robust Speech Recognition}, volume={16},
    DOI={<a href="https://doi.org/10.1109/TASL.2008.925879">10.1109/TASL.2008.925879</a>},
    number={5}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
    author={Ion, Valentin and Haeb-Umbach, Reinhold}, year={2008}, pages={1047–1060}
    }'
  chicago: 'Ion, Valentin, and Reinhold Haeb-Umbach. “A Novel Uncertainty Decoding
    Rule With Applications to Transmission Error Robust Speech Recognition.” <i>IEEE
    Transactions on Audio, Speech, and Language Processing</i> 16, no. 5 (2008): 1047–60.
    <a href="https://doi.org/10.1109/TASL.2008.925879">https://doi.org/10.1109/TASL.2008.925879</a>.'
  ieee: V. Ion and R. Haeb-Umbach, “A Novel Uncertainty Decoding Rule With Applications
    to Transmission Error Robust Speech Recognition,” <i>IEEE Transactions on Audio,
    Speech, and Language Processing</i>, vol. 16, no. 5, pp. 1047–1060, 2008.
  mla: Ion, Valentin, and Reinhold Haeb-Umbach. “A Novel Uncertainty Decoding Rule
    With Applications to Transmission Error Robust Speech Recognition.” <i>IEEE Transactions
    on Audio, Speech, and Language Processing</i>, vol. 16, no. 5, 2008, pp. 1047–60,
    doi:<a href="https://doi.org/10.1109/TASL.2008.925879">10.1109/TASL.2008.925879</a>.
  short: V. Ion, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
    Processing 16 (2008) 1047–1060.
date_created: 2019-07-12T05:28:53Z
date_updated: 2022-01-06T06:51:10Z
department:
- _id: '54'
doi: 10.1109/TASL.2008.925879
intvolume: '        16'
issue: '5'
keyword:
- automatic speech recognition
- bit errors
- codecs
- communication links
- corrupted observations
- decoding
- distributed speech recognition
- error-prone communication network
- feature vector sequence
- hidden Markov model-based ASR
- hidden Markov models
- inter-frame correlation
- Internet telephony
- network speech recognition
- packet loss
- speech posterior
- speech recognition
- transmission error robust speech recognition
- uncertainty decoding
- voice-over-IP codecs
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2008/IoHa08-1.pdf
oa: '1'
page: 1047-1060
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: A Novel Uncertainty Decoding Rule With Applications to Transmission Error Robust
  Speech Recognition
type: journal_article
user_id: '44006'
volume: 16
year: '2008'
...
---
_id: '11931'
abstract:
- lang: eng
  text: The paper is concerned with binaural signal processing for a bimodal human-robot
    interface with hearing and vision. The two microphone signals are processed to
    obtain an enhanced single-channel input signal for the subsequent speech recognizer
    and to localize the acoustic source, an important information for establishing
    a natural human-robot communication. We utilize a robust adaptive algorithm for
    filter-and-sum beamforming (FSB) and extract speaker direction information from
    the resulting FIR filter coefficients. Further, particle filtering is applied
    which conducts a nonlinear Bayesian tracking of speaker movement. Good location
    accuracy can be achieved even in highly reverberant environments. The results
    obtained outperform the conventional generalized cross correlation (GCC) method.
author:
- 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: 'Warsitz E, Haeb-Umbach R. Robust speaker direction estimation with particle
    filtering. In: <i>IEEE Workshop on Multimedia Signal Processing (MMSP 2004)</i>.
    ; 2004:367-370. doi:<a href="https://doi.org/10.1109/MMSP.2004.1436569">10.1109/MMSP.2004.1436569</a>'
  apa: Warsitz, E., &#38; Haeb-Umbach, R. (2004). Robust speaker direction estimation
    with particle filtering. In <i>IEEE Workshop on Multimedia Signal Processing (MMSP
    2004)</i> (pp. 367–370). <a href="https://doi.org/10.1109/MMSP.2004.1436569">https://doi.org/10.1109/MMSP.2004.1436569</a>
  bibtex: '@inproceedings{Warsitz_Haeb-Umbach_2004, title={Robust speaker direction
    estimation with particle filtering}, DOI={<a href="https://doi.org/10.1109/MMSP.2004.1436569">10.1109/MMSP.2004.1436569</a>},
    booktitle={IEEE Workshop on Multimedia Signal Processing (MMSP 2004)}, author={Warsitz,
    Ernst and Haeb-Umbach, Reinhold}, year={2004}, pages={367–370} }'
  chicago: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Robust Speaker Direction Estimation
    with Particle Filtering.” In <i>IEEE Workshop on Multimedia Signal Processing
    (MMSP 2004)</i>, 367–70, 2004. <a href="https://doi.org/10.1109/MMSP.2004.1436569">https://doi.org/10.1109/MMSP.2004.1436569</a>.
  ieee: E. Warsitz and R. Haeb-Umbach, “Robust speaker direction estimation with particle
    filtering,” in <i>IEEE Workshop on Multimedia Signal Processing (MMSP 2004)</i>,
    2004, pp. 367–370.
  mla: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Robust Speaker Direction Estimation
    with Particle Filtering.” <i>IEEE Workshop on Multimedia Signal Processing (MMSP
    2004)</i>, 2004, pp. 367–70, doi:<a href="https://doi.org/10.1109/MMSP.2004.1436569">10.1109/MMSP.2004.1436569</a>.
  short: 'E. Warsitz, R. Haeb-Umbach, in: IEEE Workshop on Multimedia Signal Processing
    (MMSP 2004), 2004, pp. 367–370.'
date_created: 2019-07-12T05:31:01Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/MMSP.2004.1436569
keyword:
- bimodal human-robot interface
- binaural signal processing
- enhanced single-channel input signal
- filter-and-sum beamforming
- filtering theory
- FIR filter coefficient
- generalized cross correlation method
- microphones
- microphone signal
- nonlinear Bayesian tracking
- particle filtering
- robust adaptive algorithm
- robust speaker direction estimation
- signal processing
- speech enhancement
- speech recognition
- speech recognizer
- user interfaces
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2004/WaHa04.pdf
oa: '1'
page: 367-370
publication: IEEE Workshop on Multimedia Signal Processing (MMSP 2004)
status: public
title: Robust speaker direction estimation with particle filtering
type: conference
user_id: '44006'
year: '2004'
...
---
_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'
...