Machine demonstrates superhuman speech recognition abilities. 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
Contact: Eric Mankin
University of Southern California