  Utilizing air-traffic control (ATC) data for downstream natural-language
processing tasks requires preprocessing steps. Key steps are the transcription
of the data via automatic speech recognition (ASR) and speaker diarization,
respectively speaker role detection (SRD) to divide the transcripts into pilot
and air-traffic controller (ATCO) transcripts. While traditional approaches
take on these tasks separately, we propose a transformer-based joint ASR-SRD
system that solves both tasks jointly while relying on a standard ASR
architecture. We compare this joint system against two cascaded approaches for
ASR and SRD on multiple ATC datasets. Our study shows in which cases our joint
system can outperform the two traditional approaches and in which cases the
other architectures are preferable. We additionally evaluate how acoustic and
lexical differences influence all architectures and show how to overcome them
for our joint architecture.
