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Sound research at acoustical society meeting
Date:10/18/2011

n Tuesday, Nov. 1.

Abstract: http://asa.aip.org/web2/asa/abstracts/search.oct11/asa179.html

Teaching automatic speech recognition engines what humans already know: Sorting sound into intelligible speech is a seemingly effortless feat. Healthy human ear-brain auditory systems perform it heroically, even in highly confusing soundscapes with a riotous mix of acoustic signals. And while scientists have successfully constructed automatic speech recognition engines to help perform routine speech transactions, they have problems and, too often, garble prevails. A team from the International Computer Science Institute at the University of California, Berkeley, and from the University of Oldenburg, Germany, is learning from errors in human speech recognition and applying that knowledge to design new signal processing strategies and models for automatic speech recognition. One key finding: automated recognition does not handle time cues in language as well as human recognition does. Improving computer models to optimize the way automated processes time variation in speech to align it with the human system could have beneficial applications in many human-machine interfaces. These range from improved hearing aids and smart homes to new assistive hearing apps on smart phones. Explains lead researcher, Bernd T. Meyer, Ph.D.: "Automatic speech recognition has its flaws. In comparison, human speech recognition works a lot better, so we thought, 'Why don't we try to learn from the auditory system and integrate those principles into automatic recognition?'" The presentation 2pSCa2, "Improving automatic speech recognition by learning from human errors," will be in the afternoon session on Tuesday, Nov. 1.

Abstract: http://asa.aip.org/web2/asa/abstracts/search.oct11/asa379.html

Highlights: Wednesday, Novem
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Contact: Charles E. Blue
cblue@aip.org
301-209-3091
American Institute of Physics
Source:Eurekalert

Page: 1 2 3 4 5 6 7 8 9 10 11

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