  Advances in Automatic Speech Recognition (ASR) over the last decade opened
new areas of speech-based automation such as in Air-Traffic Control (ATC)
environment. Currently, voice communication and data links communications are
the only way of contact between pilots and Air-Traffic Controllers (ATCo),
where the former is the most widely used and the latter is a non-spoken method
mandatory for oceanic messages and limited for some domestic issues. ASR
systems on ATCo environments inherit increasing complexity due to accents from
non-English speakers, cockpit noise, speaker-dependent biases, and small
in-domain ATC databases for training. Hereby, we introduce CleanSky EC-H2020
ATCO2, a project that aims to develop an ASR-based platform to collect,
organize and automatically pre-process ATCo speech-data from air space. This
paper conveys an exploratory benchmark of several state-of-the-art ASR models
trained on more than 170 hours of ATCo speech-data. We demonstrate that the
cross-accent flaws due to speakers' accents are minimized due to the amount of
data, making the system feasible for ATC environments. The developed ASR system
achieves an averaged word error rate (WER) of 7.75% across four databases. An
additional 35% relative improvement in WER is achieved on one test set when
training a TDNNF system with byte-pair encoding.
