---
_id: '63157'
abstract:
- lang: eng
  text: Three-phase cascaded H-bridge converters (CHBs) in star configuration require
    reliable current controllers to evenly charge the module DC-link capacitors. Conventionally,
    a current control in dq-coordinates is utilized. At steady state, the resulting
    calculated reference arm voltages are sinusoidal, have identical amplitudes and
    show a phase shift of 120 degree to each other. For balanced grid inductors, the
    resulting grid currents also have the same amplitude. However, own simulations
    show that unbalanced grid inductors always lead to different grid current amplitudes
    (4% difference in this case). As a result, the averaged charging module powers
    differ and the peak DC-link capacitor voltage rises as well. In the first step,
    an adaptation of an existing zero-sequence voltage injection is proposed. For
    balanced grid inductors, it converges to the 3rd harmonic voltage injection which
    can reduce the peak-to-peak DC-link voltage ripple up by to 50% and balances the
    power between the phases. However, unbalanced grid inductors still lead to the
    same unbalanced grid currents of 4%. Therefore, a new method with 4 integrators
    based on linear regression is proposed to achieve sinusoidal grid currents for
    unbalanced inductors. The proposed method has a similar transient dynamic as the
    conventional dq control, but balances the grid currents nearly ideally. Simulation
    results of a 1MW cascaded H bridge and a scaled-down prototype verify the proposed
    method.
author:
- first_name: Roland
  full_name: Unruh, Roland
  id: '34289'
  last_name: Unruh
- first_name: Joachim
  full_name: Böcker, Joachim
  id: '66'
  last_name: Böcker
  orcid: 0000-0002-8480-7295
- first_name: Frank
  full_name: Schafmeister, Frank
  id: '71291'
  last_name: Schafmeister
citation:
  ama: 'Unruh R, Böcker J, Schafmeister F. Three-Phase Instantaneous Current Controller
    for Unbalanced Grid Inductors Without DQ Transform for Cascaded H-Bridge Converters.
    In: <i>2025 Energy Conversion Congress &#38;amp;Amp; Expo Europe (ECCE Europe)</i>.
    IEEE; 2025. doi:<a href="https://doi.org/10.1109/ecce-europe62795.2025.11238538">10.1109/ecce-europe62795.2025.11238538</a>'
  apa: Unruh, R., Böcker, J., &#38; Schafmeister, F. (2025). Three-Phase Instantaneous
    Current Controller for Unbalanced Grid Inductors Without DQ Transform for Cascaded
    H-Bridge Converters. <i>2025 Energy Conversion Congress &#38;amp;Amp; Expo Europe
    (ECCE Europe)</i>. 2025 Energy Conversion Congress &#38; Expo Europe (ECCE Europe),
    Birmingham, United Kingdom. <a href="https://doi.org/10.1109/ecce-europe62795.2025.11238538">https://doi.org/10.1109/ecce-europe62795.2025.11238538</a>
  bibtex: '@inproceedings{Unruh_Böcker_Schafmeister_2025, title={Three-Phase Instantaneous
    Current Controller for Unbalanced Grid Inductors Without DQ Transform for Cascaded
    H-Bridge Converters}, DOI={<a href="https://doi.org/10.1109/ecce-europe62795.2025.11238538">10.1109/ecce-europe62795.2025.11238538</a>},
    booktitle={2025 Energy Conversion Congress &#38;amp;amp; Expo Europe (ECCE Europe)},
    publisher={IEEE}, author={Unruh, Roland and Böcker, Joachim and Schafmeister,
    Frank}, year={2025} }'
  chicago: Unruh, Roland, Joachim Böcker, and Frank Schafmeister. “Three-Phase Instantaneous
    Current Controller for Unbalanced Grid Inductors Without DQ Transform for Cascaded
    H-Bridge Converters.” In <i>2025 Energy Conversion Congress &#38;amp;Amp; Expo
    Europe (ECCE Europe)</i>. IEEE, 2025. <a href="https://doi.org/10.1109/ecce-europe62795.2025.11238538">https://doi.org/10.1109/ecce-europe62795.2025.11238538</a>.
  ieee: 'R. Unruh, J. Böcker, and F. Schafmeister, “Three-Phase Instantaneous Current
    Controller for Unbalanced Grid Inductors Without DQ Transform for Cascaded H-Bridge
    Converters,” presented at the 2025 Energy Conversion Congress &#38; Expo Europe
    (ECCE Europe), Birmingham, United Kingdom, 2025, doi: <a href="https://doi.org/10.1109/ecce-europe62795.2025.11238538">10.1109/ecce-europe62795.2025.11238538</a>.'
  mla: Unruh, Roland, et al. “Three-Phase Instantaneous Current Controller for Unbalanced
    Grid Inductors Without DQ Transform for Cascaded H-Bridge Converters.” <i>2025
    Energy Conversion Congress &#38;amp;Amp; Expo Europe (ECCE Europe)</i>, IEEE,
    2025, doi:<a href="https://doi.org/10.1109/ecce-europe62795.2025.11238538">10.1109/ecce-europe62795.2025.11238538</a>.
  short: 'R. Unruh, J. Böcker, F. Schafmeister, in: 2025 Energy Conversion Congress
    &#38;amp;Amp; Expo Europe (ECCE Europe), IEEE, 2025.'
conference:
  end_date: 2025-09-04
  location: Birmingham, United Kingdom
  name: 2025 Energy Conversion Congress & Expo Europe (ECCE Europe)
  start_date: 2025-09-01
date_created: 2025-12-16T15:20:55Z
date_updated: 2025-12-16T15:25:38Z
department:
- _id: '52'
doi: 10.1109/ecce-europe62795.2025.11238538
keyword:
- Cascaded H-Bridge
- Current Control
- dq Transformation
- Linear Regression
- Unbalanced Inductors
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/11238538
publication: 2025 Energy Conversion Congress &amp;amp; Expo Europe (ECCE Europe)
publication_status: published
publisher: IEEE
status: public
title: Three-Phase Instantaneous Current Controller for Unbalanced Grid Inductors
  Without DQ Transform for Cascaded H-Bridge Converters
type: conference
user_id: '34289'
year: '2025'
...
---
_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: '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'
...