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Automatic Speech Recognition (ASR), as the assistance of speech communicationbetween pilots and air-traffic controllers, can significantly reduce thecomplexity of the task and increase the reliability of transmitted information.ASR application can lead to a lower number of incidents caused bymisunderstanding and improve air traffic management (ATM) efficiency.Evidently, high accuracy predictions, especially, of key information, i.e.,callsigns and commands, are required to minimize the risk of errors. We provethat combining the benefits of ASR and Natural Language Processing (NLP)methods to make use of surveillance data (i.e. additional modality) helps toconsiderably improve the recognition of callsigns (named entity). In thispaper, we investigate a two-step callsign boosting approach: (1) at the 1 step(ASR), weights of probable callsign n-grams are reduced in G.fst and/or in thedecoding FST (lattices), (2) at the 2 step (NLP), callsigns extracted from theimproved recognition outputs with Named Entity Recognition (NER) are correlatedwith the surveillance data to select the most suitable one. Boosting callsignn-grams with the combination of ASR and NLP methods eventually leads up to53.7% of an absolute, or 60.4% of a relative, improvement in callsignrecognition.

A two-step approach to leverage contextual data: speech recognition in air-traffic communications