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- 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.
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- 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.
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- Potentially, the system's novel underlying principles
could have applications in such medical areas as patient monitoring and
the reading of electrocardiograms.
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- 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.
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- 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.
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- 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.
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- The system can distinguish 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.
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- 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.
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- 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.
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- With just a minor adjustment, the system can identify
different speakers of the same word with superhuman acuity.
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- 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.
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- 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.
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- "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.
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- "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."
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- Remarkably, USC's neural net system uses an architecture
consisting of just 11 neurons connected by a mere 30 links.
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- 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.
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- "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.
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- "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."
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- 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.
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- "You might say, we let our cells hear the music,"
Berger said.
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- 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.
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- 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.
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- 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. - By Eric Mankin
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- (Editor's Note: A demonstration of the Berger-Liaw Neural
Network Speaker-Independent Speech Recognition System can be found on line
at this URL http://unisci.com/stories/19994/1001991.htm)
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