Call-sign recognition and understanding for noisy air-traffic transcripts using surveillance information
This addresses the challenge of noisy communication in air traffic control, improving call-sign extraction for operational safety, though it appears incremental as it builds on existing recognition methods with added data and surveillance integration.
The paper tackles the problem of extracting call-signs from noisy air-traffic control transcripts by proposing a new recognition system that incorporates surveillance information, achieving up to a fourfold increase in call-sign accuracy.
Air traffic control (ATC) relies on communication via speech between pilot and air-traffic controller (ATCO). The call-sign, as unique identifier for each flight, is used to address a specific pilot by the ATCO. Extracting the call-sign from the communication is a challenge because of the noisy ATC voice channel and the additional noise introduced by the receiver. A low signal-to-noise ratio (SNR) in the speech leads to high word error rate (WER) transcripts. We propose a new call-sign recognition and understanding (CRU) system that addresses this issue. The recognizer is trained to identify call-signs in noisy ATC transcripts and convert them into the standard International Civil Aviation Organization (ICAO) format. By incorporating surveillance information, we can multiply the call-sign accuracy (CSA) up to a factor of four. The introduced data augmentation adds additional performance on high WER transcripts and allows the adaptation of the model to unseen airspaces.