USC biomedical engineers have created the world's first machine system that can recognize spoken words better than humans can.

The system might soon facilitate voice control of computers and other machines, help the deaf and instantly produce clean transcripts of conversations. The U.S. Navy, which listens for the sounds of submarines in the hubbub of the open seas, is another possible user.

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. 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.

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, says system co-designer Theodore W. Berger, a professor of biomedical engineering.

The system can distinguish words in vast amounts of random "white noise" and can pluck words from the background clutter of other voices-the hubbub heard in bus stations 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 achieved this improved performance by paying closer attention to the signal characteristics used by real flesh-and-blood brains in processing information.

"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. "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 only 30 links.

A demonstration of the Berger-Liaw Neural Network Speaker-Independent Speech Recognition System can be found online at: http://www. usc. edu/ext-relations/news_ service/real/real_video.html