DARPA launches RATS program for advanced speech-recognition algorithms in noisy conditions

Posted by John Keller

ARLINGTON, Va., 14 Feb. 2010. Scientists at the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., are launching a program to develop speech transcription, translation, and, speech signal processing technologies that function effectively in noisy places to support intelligence gathering.

DARPA issued a broad agency announcement Friday (solicitation number DARPA-BAA-10-34) for a human language technology research and development program called Robust Automatic Transcription of Speech (RATS) to determine speech activity; identify speakers and languages; and spot key words in extremely noisy field conditions.

DARPA wants innovative approaches that enable revolutionary advances in science, devices, or systems able to gather intelligence from speech received from weak and/or noisy communications channels. RATS test and training data will be collected under both controlled and uncontrolled field conditions.

Existing transcription and translation and speech signal processing technologies are insufficient for working with noisy or degraded speech signals that are of importance to current and future U.S. Department of Defense (DOD) operations, DARPA officials explain.

Since no technological solution exists today to analyze this kind of noisy and distorted speech, operational units must allocate significant human resources for this task. Even so, operators frequently search blindly over thousands of possible channels at any given moment.

For this project, DARPA experts want to develop algorithms for speech detection, language detection, speaker identification, and key word spotting.

Companies interested in participating in the RATS program should respond no later than 29 July 2010. For questions or concerns, contact DARPA's Joseph Olive by e-mail at [email protected] or by fax at 703-807-4949.

More information is online at https://www.fbo.gov/index?s=opportunity&mode=form&id=484d5c77373310d7745963cf27c40cf7&tab=core&_cview=0.

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