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dc.contributor.authorKarlsson, Joakim
dc.contributor.otherMassachusetts Institute of Technology. Flight Transportation Laboratoryen_US
dc.date.accessioned2012-01-06T22:27:17Z
dc.date.available2012-01-06T22:27:17Z
dc.date.issued1990en_US
dc.identifier21336744en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/68122
dc.descriptionJanuary 1990en_US
dc.descriptionAlso issued as an M.S. thesis, Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1990en_US
dc.descriptionIncludes bibliographical references (p. 89-94)en_US
dc.description.abstractToday, the Air Traffic Control (ATC) system relies primarily on verbal communication between the air traffic controllers and the pilots of the aircraft in the controlled airspace. Although a computer system exists that processes primary radar, secondary radar, and flight plan information, the information contained within the verbal communications is not retained. The introduction of Automatic Speech Recognition (ASR) technology would allow this information to be captured for processing. The research presented in this paper examines the feasibility of using ASR technology in the Air Traffic Control environment. The current status of the technology is assessed. Problems that are unique to ATC applications of voice input are identified. Since ASR technology is inherently a part of the man-machine interface between the user and the system, emphasis is placed on the relevant human factors issues. A man-machine model is presented which demonstrates the use of mixed input modalities, automatic error detection and correction techniques, and the optimal use of feedback to the controller. Much of the potential benefit of introducing ASR technology into the Air Traffic Control system is a result of the highly constrained language used by air traffic controllers. Consequently, the information content of the ATC language must be determined, and methods must be designed to process the various levels of knowledge inherently available in ATC communications. The man machine model adopted in this paper demonstrates techniques to utilize syntactic, semantic, and pragmatic information to improve overall recognition accuracy. An intelligent, adaptive voice input parser is presented.en_US
dc.description.sponsorshipResearch sponsored by the FAA/NASA Joint University Program for Air Transportation Research.en_US
dc.format.extent94 pen_US
dc.publisherCambridge, Mass. : Flight Transportation Laboratory, Dept. of Aeronautics and Astronautics, Massachusetts Institute of Technology, [1990]en_US
dc.relation.ispartofseriesFTL report (Massachusetts Institute of Technology. Flight Transportation Laboratory) ; R90-1en_US
dc.subjectAir traffic controlen_US
dc.subjectAutomatic speech recognitionen_US
dc.subjectAutomationen_US
dc.titleThe integration of Automatic Speech Recognition into the Air Traffic Control systemen_US
dc.title.alternativeIntegration of ASR into the ATC systemen_US
dc.typeTechnical Reporten_US


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