Detail výsledku
Call-Sign Recognition and Understanding for Noisy Air-Traffic Transcripts Using Surveillance Information
Kocour Martin, Ing., UPGM (FIT)
Veselý Karel, Ing., Ph.D., UPGM (FIT)
Szőke Igor, Ing., Ph.D., UPGM (FIT)
KLAKOW, D.
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.
Air Traffic Control, Call-sign Recognition, Context Incorporation, Data Augmentation
@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"
}