  Automatic speech recognition (ASR) allows transcribing the communications
between air traffic controllers (ATCOs) and aircraft pilots. The transcriptions
are used later to extract ATC named entities, e.g., aircraft callsigns. One
common challenge is speech activity detection (SAD) and speaker diarization
(SD). In the failure condition, two or more segments remain in the same
recording, jeopardizing the overall performance. We propose a system that
combines SAD and a BERT model to perform speaker change detection and speaker
role detection (SRD) by chunking ASR transcripts, i.e., SD with a defined
number of speakers together with SRD. The proposed model is evaluated on
real-life public ATC databases. Our BERT SD model baseline reaches up to 10%
and 20% token-based Jaccard error rate (JER) in public and private ATC
databases. We also achieved relative improvements of 32% and 7.7% in JERs and
SD error rate (DER), respectively, compared to VBx, a well-known SD system.
