Hello listers,
I thought many of you would find this press release from the University of
Southern California fascinating.
Enjoy.
Bryan Bashin
> >From the University of Southern California News Service
>3620 South Vermont Avenue, Los Angeles, CA 90089-2538
>Tel: 213 740 2215 Fax: 213 740 7600 Email: news_service@usc.edu
>http://uscnews.usc.edu
>
>
>Contact: Eric Mankin (213/740-9344) 0999025
> email: mankin@usc.edu
>
>
>
>MACHINE DEMONSTRATES SUPERHUMAN
>SPEECH RECOGNITION ABILITIES
>
>Note: A demonstration of the Berger-Liaw Neural Network Speaker-
>Independent Speech Recognition System can be found on line at:
>
>http://www.usc.edu/ext-relations/news_service/real/real_video.html
>
>
>University of Southern California biomedical engineers
>have created the world's first machine system that
>can recognize spoken words better than humans can.
>A fundamental rethinking of a long-underperforming
>computer architecture led to their achievement.
>
>The system might soon facilitate voice control of computers
>and other machines, help the deaf, aid air traffic controllers
>and others who must understand speech in noisy
>environments, and instantly produce clean transcripts of
>conversations, identifying each of the speakers. The U.S.
>Navy, which listens for the sounds of submarines in the
>hubbub of the open seas, is another possible user.
>
>Potentially, the system's novel underlying principles could
>have applications in such medical areas as patient
>monitoring and the reading of electrocardiograms.
>
>In benchmark testing using just a few spoken words, USC's
>Berger-Liaw Neural Network Speaker Independent Speech
>Recognition System not only bested all existing computer
>speech recognition systems but outperformed the keenest
>human ears.
>
>Neural nets are computing devices that mimic the way
>brains process information. Speaker-independent systems
>can recognize a word no matter who or what pronounces it.
>
>No previous speaker-independent computer system has
>ever outperformed humans in recognizing spoken
>language, even in very small test bases, says system co-
>designer Theodore W. Berger, Ph.D., a professor of
>biomedical engineering in the USC School of Engineering.
>
>The system can distinguished words in vast amounts of
>random "white" noise -- noise with amplitude 1,000 times
>the strength of the target auditory signal. Human listeners
>can deal with only a fraction as much.
>
>And the system can pluck words from the background
>clutter of other voices -- the hubbub heard in bus stations,
>theater lobbies and cocktail parties, for example.
>
>Even the best existing systems fail completely when as
>little as 10 percent of hubbub masks a speaker's voice.
>At slightly higher noise levels, the likelihood that a human
>listener can identify spoken test words is mere chance. By
>contrast, Berger and Liaw's system functions at 60 percent
>recognition with a hubbub level 560 times the strength of
>the target stimulus.
>
>With just a minor adjustment, the system can identify
>different speakers of the same word with superhuman
>acuity.
>
>Berger and system co-designer Jim-Shih Liaw, Ph.D.,
>achieved this improved performance by paying closer
>attention to the signal characteristics used by real flesh-
>and-blood brains in processing information.
>
>First proposed in the 1940s and the subject of intensive
>research in the '80s and early '90s, neural nets are
>computers configured to imitate the brain's system of
>information processing, wherein data are structured not
>by a central processing unit but by an interlinked network
>of simple units called neurons. Rather than being
>programmed, neural nets learn to do tasks through a
>training regimen in which desired responses to stimuli are
>reinforced and unwanted ones are not.
>
>"Though mathematical theorists demonstrated that nets
>should be highly effective for certain kinds of computation
>(particularly pattern recognition), it has been difficult for
>artificial neural networks even to approach the power of
>biological systems," said Liaw, director of the Laboratory for
>Neural Dynamics and a research assistant professor of
>biomedical engineering at the USC School of Engineering.
>
>"Even large nets with more than 1,000 neurons and 10,000
>interconnections have shown lackluster results compared
>with theoretical capabilities. Deficiencies were often laid to
>the fact that even 1,000-neuron networks are tiny,
>compared with the millions or billions of neurons in
>biological systems."
>
>Remarkably, USC's neural net system uses an architecture
>consisting of just 11 neurons connected by a mere 30 links.
>
>According to Berger, who has spent years studying
>biological data-processing systems, previous computer
>neural nets went wrong by oversimplifying their biological
>models, omitting a crucial dimension.
>
>"Neurons process information structured in time," he
>explained. "They communicate with one another in a
>'language' whereby the 'meaning' imparted to the receiving
>neuron is coded into the signal's timing. A pair of pulses
>separated by a certain time interval excites a certain
>neuron, while a pair of pulses separated by a shorter or
>longer interval inhibits it.
>
>"So far," Berger continued, "efforts to create neural networks
>have had silicon neurons transmitting only discreet signals
>of varying intensity, all clocked the way a computer is
>clocked, in beats of unvarying duration. But in living cells,
>the temporal dimension, both in the exciting signal and in
>the response, is as important as the intensity."
>
>Berger and Liaw created computer chip neurons that
>closely mimic the signaling behavior of living cells --
>those of the hippocampus, the brain structure involved
>in associative learning.
>
>"You might say, we let our cells hear the music," Berger
>said.
>
>Berger and Liaw's computer chip neurons were combined
>into a small neural network using standard architecture.
>While all the neurons shared the same hippocampus-
>mimicking general characteristics, each was randomly
>given slightly different individual characteristics, in much
>the same way that individual hippocampus neurons would
>have slightly different individual characteristics.
>
>The network created was then trained, using a procedure
>as unique as the neurons -- again taken from the biological
>model, a learning rule that allows the temporal properties of
>the net connections to change.
>
>The USC research was funded by the Office of Naval
>Research; the Defense Department's Advanced Research
>Projects Agency; the National Centers for Research
>Resources, and the National Institute of Mental Health.
>The university has applied for a patent on the system and
>the architectural concepts on which it is based.
>
>EM.BERGER99 -USC- SEPT. 30, 1999
>
>
>
>James Lytle, Editor
>USC News Service
>Phone: (213) 740-4751
>Fax: (213) 740-7600
>Email: jlytle@usc.edu
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