World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
7013
World Ranking
13389
National Ranking
849

Jon Barker publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Jon Barker sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 150 publications — 27th percentile

27% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Jon Barker D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Jon Barker sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 31 D-Index — 6th percentile

6% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Jon Barker is affiliated with the University of Sheffield in the United Kingdom. Their research primarily focuses on computer science, with a strong emphasis on signal processing within this domain. The main subfields covered by their work include signal processing, artificial intelligence, cognitive neuroscience, speech and hearing, and physiology.

The scientist's research topics encompass several areas related to speech and audio technologies. These topics include speech and audio processing, speech recognition and synthesis, hearing loss and rehabilitation, voice and speech disorders, music and audio processing, noise effects and management, and phonetics and phonology research.

Jon Barker has contributed to multiple recent publications. Notable papers include:

  • CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings, 2020, arXiv (Cornell University)
  • The 1st Clarity Prediction Challenge: A machine learning challenge for hearing aid intelligibility prediction, 2022, Interspeech 2022
  • Dataset of British English speech recordings for psychoacoustics and speech processing research: The clarity speech corpus, 2022, Data in Brief
  • Acoustic Modelling From Raw Source and Filter Components for Dysarthric Speech Recognition, 2022, IEEE/ACM Transactions on Audio Speech and Language Processing
  • Multi-Modal Acoustic-Articulatory Feature Fusion For Dysarthric Speech Recognition, 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The venues where Jon Barker frequently publishes include arXiv (Cornell University), Interspeech 2022, The Journal of the Acoustical Society of America, Zenodo (CERN European Organization for Nuclear Research), and ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing.

In collaborative work, Jon Barker often coauthors publications with researchers such as Michael A. Akeroyd, Trevor J. Cox, Simone Graetzer, Jennifer Firth, and Zhengjun Yue.

Best Publications

  • An audio-visual corpus for speech perception and automatic speech recognition

    Martin Cooke;Jon Barker;Stuart Cunningham;Xu Shao

  • The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines

    Jon Barker;Ricard Marxer;Emmanuel Vincent;Shinji Watanabe

  • An analysis of environment, microphone and data simulation mismatches in robust speech recognition

    Emmanuel Vincent;Shinji Watanabe;Aditya Arie Nugraha;Jon Barker

  • The second ‘chime’ speech separation and recognition challenge: Datasets, tasks and baselines

    Emmanuel Vincent;Jon Barker;Shinji Watanabe;Jonathan Le Roux

  • The foreign language cocktail party problem: Energetic and informational masking effects in non-native speech perception

    Martin Cooke;M. L. Garcia Lecumberri;Jon Barker

  • CHiME-6 Challenge: Tackling multispeaker speech recognition for unsegmented recordings

    Shinji Watanabe;Michael Mandel;Jon Barker;Emmanuel Vincent

  • The Fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, Task and Baselines.

    Jon Barker;Shinji Watanabe;Emmanuel Vincent;Jan Trmal

  • The PASCAL CHiME speech separation and recognition challenge

    Jon Barker;Emmanuel Vincent;Ning Ma;Heidi Christensen

  • DECODING SPEECH IN THE PRESENCE OF OTHER SOURCES

    Jon P. Barker;Martin P. Cooke;Daniel P. W. Ellis

  • Soft decisions in missing data techniques for robust automatic speech recognition.

    Jon Barker;Ljubomir Josifovski;Martin Cooke;Phil D. Green

  • Robust ASR Based On Clean Speech Models: An Evaluation of Missing Data Techniques For Connected Digit Recognition in Noise

    Jon Barker;Martin Cooke;Phil D. Green

  • The CHiME corpus: a resource and a challenge for computational hearing in multisource environments.

    Heidi Christensen;Jon Barker;Ning Ma;Phil D. Green

  • The third ‘CHiME’ speech separation and recognition challenge: Analysis and outcomes

    Jon Barker;Ricard Marxer;Emmanuel Vincent;Shinji Watanabe

  • Modelling speaker intelligibility in noise

    Jon Barker;Martin Cooke

  • The second ‘CHiME’ speech separation and recognition challenge: An overview of challenge systems and outcomes

    Emmanuel Vincent;Jon Barker;Shinji Watanabe;Jonathan Le Roux

  • Chime-home: A dataset for sound source recognition in a domestic environment

    Peter Foster;Siddharth Sigtia;Sacha Krstulovic;Jon Barker

  • A corpus of audio-visual Lombard speech with frontal and profile views

    Najwa Alghamdi;Steve Maddock;Ricard Marxer;Jon Barker

  • Techniques for handling convolutional distortion with `missing data' automatic speech recognition

    Kalle J Palomäki;Kalle J Palomäki;Kalle J Palomäki;Guy J Brown;Jon P Barker

  • Mask estimation for missing data speech recognition based on statistics of binaural interaction

    S. Harding;J. Barker;G.J. Brown

  • Exploiting correlogram structure for robust speech recognition with multiple speech sources

    Ning Ma;Phil Green;Jon Barker;André Coy

  • Clarity-2021 challenges : machine learning challenges for advancing hearing aid processing

    Simone Graetzer;Jon Barker;Trevor J. Cox;Michael Akeroyd

Frequent Co-Authors

Martin Cooke
Martin Cooke Ikerbasque
Emmanuel Vincent
Emmanuel Vincent University of Lorraine
Guy J. Brown
Guy J. Brown University of Sheffield
Thomas Hain
Thomas Hain University of Sheffield
Shinji Watanabe
Shinji Watanabe Carnegie Mellon University
Daniel P. W. Ellis
Daniel P. W. Ellis Google (United States)
Jonathan Le Roux
Jonathan Le Roux Mitsubishi Electric (United States)
Amir Hussain
Amir Hussain Edinburgh Napier University
Takuya Yoshioka
Takuya Yoshioka Microsoft (United States)
Sanjeev Khudanpur
Sanjeev Khudanpur Johns Hopkins University

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