  Air traffic management and specifically air-traffic control (ATC) rely mostly
on voice communications between Air Traffic Controllers (ATCos) and pilots. In
most cases, these voice communications follow a well-defined grammar that could
be leveraged in Automatic Speech Recognition (ASR) technologies. The callsign
used to address an airplane is an essential part of all ATCo-pilot
communications. We propose a two-steps approach to add contextual knowledge
during semi-supervised training to reduce the ASR system error rates at
recognizing the part of the utterance that contains the callsign. Initially, we
represent in a WFST the contextual knowledge (i.e. air-surveillance data) of an
ATCo-pilot communication. Then, during Semi-Supervised Learning (SSL) the
contextual knowledge is added by second-pass decoding (i.e. lattice
re-scoring). Results show that `unseen domains' (e.g. data from airports not
present in the supervised training data) are further aided by contextual SSL
when compared to standalone SSL. For this task, we introduce the Callsign Word
Error Rate (CA-WER) as an evaluation metric, which only assesses ASR
performance of the spoken callsign in an utterance. We obtained a 32.1% CA-WER
relative improvement applying SSL with an additional 17.5% CA-WER improvement
by adding contextual knowledge during SSL on a challenging ATC-based test set
gathered from LiveATC.
