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. 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.
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.