---
_id: '48877'
abstract:
- lang: eng
  text: OpenML is an online machine learning platform where researchers can easily
    share data, machine learning tasks and experiments as well as organize them online
    to work and collaborate more efficiently. In this paper, we present an R package
    to interface with the OpenML platform and illustrate its usage in combination
    with the machine learning R package mlr (Bischl et al. J Mach Learn Res 17(170):1—5,
    2016). We show how the OpenML package allows R users to easily search, download
    and upload data sets and machine learning tasks. Furthermore, we also show how
    to upload results of experiments, share them with others and download results
    from other users. Beyond ensuring reproducibility of results, the OpenML platform
    automates much of the drudge work, speeds up research, facilitates collaboration
    and increases the users’ visibility online.
author:
- first_name: Giuseppe
  full_name: Casalicchio, Giuseppe
  last_name: Casalicchio
- first_name: Jakob
  full_name: Bossek, Jakob
  id: '102979'
  last_name: Bossek
  orcid: 0000-0002-4121-4668
- first_name: Michel
  full_name: Lang, Michel
  last_name: Lang
- first_name: Dominik
  full_name: Kirchhoff, Dominik
  last_name: Kirchhoff
- first_name: Pascal
  full_name: Kerschke, Pascal
  last_name: Kerschke
- first_name: Benjamin
  full_name: Hofner, Benjamin
  last_name: Hofner
- first_name: Heidi
  full_name: Seibold, Heidi
  last_name: Seibold
- first_name: Joaquin
  full_name: Vanschoren, Joaquin
  last_name: Vanschoren
- first_name: Bernd
  full_name: Bischl, Bernd
  last_name: Bischl
citation:
  ama: 'Casalicchio G, Bossek J, Lang M, et al. OpenML: An R Package to Connect to
    the Machine Learning Platform OpenML. <i>Computational Statistics</i>. 2019;34(3):977–991.
    doi:<a href="https://doi.org/10.1007/s00180-017-0742-2">10.1007/s00180-017-0742-2</a>'
  apa: 'Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner,
    B., Seibold, H., Vanschoren, J., &#38; Bischl, B. (2019). OpenML: An R Package
    to Connect to the Machine Learning Platform OpenML. <i>Computational Statistics</i>,
    <i>34</i>(3), 977–991. <a href="https://doi.org/10.1007/s00180-017-0742-2">https://doi.org/10.1007/s00180-017-0742-2</a>'
  bibtex: '@article{Casalicchio_Bossek_Lang_Kirchhoff_Kerschke_Hofner_Seibold_Vanschoren_Bischl_2019,
    title={OpenML: An R Package to Connect to the Machine Learning Platform OpenML},
    volume={34}, DOI={<a href="https://doi.org/10.1007/s00180-017-0742-2">10.1007/s00180-017-0742-2</a>},
    number={3}, journal={Computational Statistics}, author={Casalicchio, Giuseppe
    and Bossek, Jakob and Lang, Michel and Kirchhoff, Dominik and Kerschke, Pascal
    and Hofner, Benjamin and Seibold, Heidi and Vanschoren, Joaquin and Bischl, Bernd},
    year={2019}, pages={977–991} }'
  chicago: 'Casalicchio, Giuseppe, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal
    Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, and Bernd Bischl.
    “OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” <i>Computational
    Statistics</i> 34, no. 3 (2019): 977–991. <a href="https://doi.org/10.1007/s00180-017-0742-2">https://doi.org/10.1007/s00180-017-0742-2</a>.'
  ieee: 'G. Casalicchio <i>et al.</i>, “OpenML: An R Package to Connect to the Machine
    Learning Platform OpenML,” <i>Computational Statistics</i>, vol. 34, no. 3, pp.
    977–991, 2019, doi: <a href="https://doi.org/10.1007/s00180-017-0742-2">10.1007/s00180-017-0742-2</a>.'
  mla: 'Casalicchio, Giuseppe, et al. “OpenML: An R Package to Connect to the Machine
    Learning Platform OpenML.” <i>Computational Statistics</i>, vol. 34, no. 3, 2019,
    pp. 977–991, doi:<a href="https://doi.org/10.1007/s00180-017-0742-2">10.1007/s00180-017-0742-2</a>.'
  short: G. Casalicchio, J. Bossek, M. Lang, D. Kirchhoff, P. Kerschke, B. Hofner,
    H. Seibold, J. Vanschoren, B. Bischl, Computational Statistics 34 (2019) 977–991.
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:51:17Z
department:
- _id: '819'
doi: 10.1007/s00180-017-0742-2
intvolume: '        34'
issue: '3'
keyword:
- Databases
- Machine learning
- R
- Reproducible research
language:
- iso: eng
page: 977–991
publication: Computational Statistics
publication_identifier:
  issn:
  - 0943-4062
status: public
title: 'OpenML: An R Package to Connect to the Machine Learning Platform OpenML'
type: journal_article
user_id: '102979'
volume: 34
year: '2019'
...
---
_id: '11937'
abstract:
- lang: eng
  text: In automatic speech recognition, hidden Markov models (HMMs) are commonly
    used for speech decoding, while switching linear dynamic models (SLDMs) can be
    employed for a preceding model-based speech feature enhancement. In this paper,
    these model types are combined in order to obtain a novel iterative speech feature
    enhancement and recognition architecture. It is shown that speech feature enhancement
    with SLDMs can be improved by feeding back information from the HMM to the enhancement
    stage. Two different feedback structures are derived. In the first, the posteriors
    of the HMM states are used to control the model probabilities of the SLDMs, while
    in the second they are employed to directly influence the estimate of the speech
    feature distribution. Both approaches lead to improvements in recognition accuracy
    both on the AURORA2 and AURORA4 databases compared to non-iterative speech feature
    enhancement with SLDMs. It is also shown that a combination with uncertainty decoding
    further enhances performance.
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. Approaches to Iterative Speech Feature Enhancement
    and Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>.
    2009;17(5):974-984. doi:<a href="https://doi.org/10.1109/TASL.2009.2014894">10.1109/TASL.2009.2014894</a>
  apa: Windmann, S., &#38; Haeb-Umbach, R. (2009). Approaches to Iterative Speech
    Feature Enhancement and Recognition. <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i>, <i>17</i>(5), 974–984. <a href="https://doi.org/10.1109/TASL.2009.2014894">https://doi.org/10.1109/TASL.2009.2014894</a>
  bibtex: '@article{Windmann_Haeb-Umbach_2009, title={Approaches to Iterative Speech
    Feature Enhancement and Recognition}, volume={17}, DOI={<a href="https://doi.org/10.1109/TASL.2009.2014894">10.1109/TASL.2009.2014894</a>},
    number={5}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
    author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2009}, pages={974–984}
    }'
  chicago: 'Windmann, Stefan, and Reinhold Haeb-Umbach. “Approaches to Iterative Speech
    Feature Enhancement and Recognition.” <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i> 17, no. 5 (2009): 974–84. <a href="https://doi.org/10.1109/TASL.2009.2014894">https://doi.org/10.1109/TASL.2009.2014894</a>.'
  ieee: S. Windmann and R. Haeb-Umbach, “Approaches to Iterative Speech Feature Enhancement
    and Recognition,” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>,
    vol. 17, no. 5, pp. 974–984, 2009.
  mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Approaches to Iterative Speech
    Feature Enhancement and Recognition.” <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i>, vol. 17, no. 5, 2009, pp. 974–84, doi:<a href="https://doi.org/10.1109/TASL.2009.2014894">10.1109/TASL.2009.2014894</a>.
  short: S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
    Processing 17 (2009) 974–984.
date_created: 2019-07-12T05:31:08Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/TASL.2009.2014894
intvolume: '        17'
issue: '5'
keyword:
- AURORA2 databases
- AURORA4 databases
- automatic speech recognition
- feedback structures
- hidden Markov models
- HMM
- iterative methods
- iterative speech feature enhancement
- model probabilities
- speech decoding
- speech enhancement
- speech feature distribution
- speech recognition
- switching linear dynamic models
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2009/WiHa09-1.pdf
oa: '1'
page: 974-984
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Approaches to Iterative Speech Feature Enhancement and Recognition
type: journal_article
user_id: '44006'
volume: 17
year: '2009'
...