  Automatic Speech Recognition (ASR) for air traffic control is generally
trained by pooling Air Traffic Controller (ATCO) and pilot data into one set.
This is motivated by the fact that pilot's voice communications are more scarce
than ATCOs. Due to this data imbalance and other reasons (e.g., varying
acoustic conditions), the speech from ATCOs is usually recognized more
accurately than from pilots. Automatically identifying the speaker roles is a
challenging task, especially in the case of the noisy voice recordings
collected using Very High Frequency (VHF) receivers or due to the
unavailability of the push-to-talk (PTT) signal, i.e., both audio channels are
mixed. In this work, we propose to (1) automatically segment the ATCO and pilot
data based on an intuitive approach exploiting ASR transcripts and (2)
subsequently consider an automatic recognition of ATCOs' and pilots' voice as
two separate tasks. Our work is performed on VHF audio data with high noise
levels, i.e., signal-to-noise (SNR) ratios below 15 dB, as this data is
recognized to be helpful for various speech-based machine-learning tasks.
Specifically, for the speaker role identification task, the module is
represented by a simple yet efficient knowledge-based system exploiting a
grammar defined by the International Civil Aviation Organization (ICAO). The
system accepts text as the input, either manually verified annotations or
automatically generated transcripts. The developed approach provides an average
accuracy in speaker role identification of about 83%. Finally, we show that
training an acoustic model for ASR tasks separately (i.e., separate models for
ATCOs and pilots) or using a multitask approach is well suited for the noisy
data and outperforms the traditional ASR system where all data is pooled
together.
