IDEAS home Printed from https://ideas.repec.org/a/adp/oattsr/v4y2020i1p5-9.html
   My bibliography  Save this article

Automatic Speech Recognition and Machine Learning for Robotic Arm in Surgery

Author

Listed:
  • J Ruby

    (Department of Medical Sciences, University of Oxford, United Kingdom
    Department of Computer Science, Stanford University, United States)

  • Susan Diana

    (Department of Medical Sciences, University of Oxford, United Kingdom)

  • Yanmin Yuan

    (Department of Bioengineering, Harvard University, United States)

  • William Harry

    (Center for Biomedical Imaging, Stanford University, United States)

  • J Tisa

    (Stanford Center for Biomedical Informatics Research, Stanford University, United States)

  • J Nedumaan

    (Department of Computer Science, Stanford University, United States)

  • Yang Yung

    (Biomedical Engineering Research Center, Nanyang Technological University, Singapore)

  • J Lepika

    (Department of Computer Science, Harvard University, United States)

  • Thomas Binford

    (Department of Computer Science, Stanford University, United States)

  • P S Jagadeesh Kumar

    (Department of Computer Science, Stanford University, United States)

  • Wenli Hu

    (Biomedical Engineering Research Center, Nanyang Technological University, Singapore)

Abstract

This article liberalizes the current machine learning rehearses as utilized in the emerging edge and as noteworthy to speech recognition approaches on present-day surgical robots...

Suggested Citation

  • J Ruby & Susan Diana & Yanmin Yuan & William Harry & J Tisa & J Nedumaan & Yang Yung & J Lepika & Thomas Binford & P S Jagadeesh Kumar & Wenli Hu, 2020. "Automatic Speech Recognition and Machine Learning for Robotic Arm in Surgery," Trends in Technical & Scientific Research, Juniper Publishers Inc., vol. 4(1), pages 5-9, March.
    • Handle: RePEc:adp:oattsr:v:4:y:2020:i:1:p:5-9
    DOI: 1019081/TTSR.2020.04.555627
    as

    Download full text from publisher

    File URL: https://juniperpublishers.com/ttsr/pdf/TTSR.MS.ID.555627.pdf
    Download Restriction: no
    File URL: https://juniperpublishers.com/ttsr/TTSR.MS.ID.555627.php
    Download Restriction: no
    File URL: https://libkey.io/1019081/TTSR.2020.04.555627?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:adp:oattsr:v:4:y:2020:i:1:p:5-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Robert Thomas (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.