  Automatic Speech Recognition (ASR) can be used as the assistance of speech
communication between pilots and air-traffic controllers. Its application can
significantly reduce the complexity of the task and increase the reliability of
transmitted information. Evidently, high accuracy predictions are needed to
minimize the risk of errors. Especially, high accuracy is required in
recognition of key information, such as commands and callsigns, used to
navigate pilots. Our results prove that the surveillance data containing
callsigns can help to considerably improve the recognition of a callsign in an
utterance when the weights of probable callsign n-grams are reduced per
utterance. In this paper, we investigate two approaches: (1) G-boosting, when
callsigns weights are adjusted at language model level (G) and followed by the
dynamic decoder with an on-the-fly composition, and (2) lattice rescoring when
callsign information is introduced on top of lattices generated using a
conventional decoder. Boosting callsign n-grams with the combination of two
methods allowed us to gain 28.4% of absolute improvement in callsign
recognition accuracy and up to 74.2% of relative improvement in WER of callsign
recognition.
