October 08, 2018

On Robustness of Cloud Speech APIs: An Early Characterization

  • Isopoussu A.
  • Kawsar F.
  • Lane N.
  • Mathur A.
  • Nadia Berthouze
  • Robert Smith

The robustness and consistency of sensory inference models under changing environmental conditions and hardware is a crucial requirement for the generalizability of recent innovative work, particularly in the field of deep learning, from the lab to the real world. We measure the extent to which current speech recognition cloud models are robust to background noise, and show that hardware variability is still a problem for real world applicability of state-of-the-art machine learning models for speech recognition.

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A Packaged 0.01-26-GHz Single-Chip SiGe Reflectometer for Two-Port Vector Network Analyzers

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© 1963-2012 IEEE. This article presents a packaged SiGe BiCMOS reflectometer for 0.01-26-GHz two-port vector network analyzers (VNAs). The reflectometer chip is composed of a resistive bridge coupler and two wideband heterodyne receivers for coherent magnitude and phase detection. In addition, a high-linearity receiver channel is designed to accommodate 20 ...

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The use of topology to protect quantum information is well-known to the condensed-matter community and, indeed, topological quantum computing is a bursting field of research and one of the competing avenues to demonstrate that quantum computers can complete certain problems that classical computers cannot. In photonics, however, we are only ...

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