Publication Details
Call-Sign Recognition and Understanding for Noisy Air-Traffic Transcripts Using Surveillance Information
Kocour Martin, Ing. (DCGM)
Veselý Karel, Ing., Ph.D. (DCGM)
Szőke Igor, Ing., Ph.D. (DCGM)
KLAKOW, D.
Air Traffic Control, Call-sign Recognition, Context Incorporation, Data Augmentation
Air traffic control (ATC) relies on communication via speech between pilot and air-traffic controller (ATCO). The call-sign, as unique identifier for each flight, is used to address a specific pilot by the ATCO. Extracting the call-sign from the communication is a challenge because of the noisy ATC voice channel and the additional noise introduced by the receiver. A low signal-to-noise ratio (SNR) in the speech leads to high word error rate (WER) transcripts. We propose a new call-sign recognition and understanding (CRU) system that addresses this issue. The recognizer is trained to identify call-signs in noisy ATC transcripts and convert them into the standard International Civil Aviation Organization (ICAO) format. By incorporating surveillance information, we can multiply the call-sign accuracy (CSA) up to a factor of four. The introduced data augmentation adds additional performance on high WER transcripts and allows the adaptation of the model to unseen airspaces.
@inproceedings{BUT178410,
author="BLATT, A. and KOCOUR, M. and VESELÝ, K. and SZŐKE, I. and KLAKOW, D.",
title="Call-Sign Recognition and Understanding for Noisy Air-Traffic Transcripts Using Surveillance Information",
booktitle="ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
year="2022",
pages="8357--8361",
publisher="IEEE Signal Processing Society",
address="Singapore",
doi="10.1109/ICASSP43922.2022.9746301",
isbn="978-1-6654-0540-9",
url="https://ieeexplore.ieee.org/document/9746301"
}