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Computer Science

D-Index
31
Citations
7013
World Ranking
13392
National Ranking
852

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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