  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.
