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A Hybrid System for Real-Time Sign Language Translation

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Manage episode 455165644 series 3194633
Content provided by SciPod. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by SciPod or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
Neuromorphic computing is a powerful tool for identifying time-varying patterns, but is often less effective than some AI-based techniques for more complex tasks. Researchers at the iCAS Lab directed by Ramtin Zand at the University of South Carolina, work on an NSF CAREER project to show how the capabilities of neuromorphic systems could be improved by blending them with specialized machine learning systems, without sacrificing their impressive energy efficiency. Using their approach, the team aims to show how the gestures of American Sign Language could be instantly translated into written and spoken language.
  continue reading

521 episodes

Artwork
iconShare
 
Manage episode 455165644 series 3194633
Content provided by SciPod. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by SciPod or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
Neuromorphic computing is a powerful tool for identifying time-varying patterns, but is often less effective than some AI-based techniques for more complex tasks. Researchers at the iCAS Lab directed by Ramtin Zand at the University of South Carolina, work on an NSF CAREER project to show how the capabilities of neuromorphic systems could be improved by blending them with specialized machine learning systems, without sacrificing their impressive energy efficiency. Using their approach, the team aims to show how the gestures of American Sign Language could be instantly translated into written and spoken language.
  continue reading

521 episodes

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