Call-sign recognition and understanding for noisy air-traffic transcripts using surveillance information

• URL: http://arxiv.org/abs/2204.06309v1
• Date: Wed, 13 Apr 2022 11:30:42 GMT
• Title: Call-sign recognition and understanding for noisy air-traffic transcripts using surveillance information
• Authors: Alexander Blatt, Martin Kocour, Karel Vesel\'y, Igor Sz\"oke, Dietrich Klakow
• Abstract summary: 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. 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.
• Score: 72.20674534231314
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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