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The Airbus Air Traffic Control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detection

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arxiv 1810.12614 v2 pith:7B45BNMX submitted 2018-10-30 cs.SD eess.AS

The Airbus Air Traffic Control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detection

Thomas Pellegrini , J\'er\^ome Farinas , Estelle Delpech , Fran\c{c}ois Lancelot This is my paper
classification cs.SD eess.AS
keywords speechchallengeairbusautomaticcallcontroldetectionrate
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In this paper, we describe the outcomes of the challenge organized and run by Airbus and partners in 2018. The challenge consisted of two tasks applied to Air Traffic Control (ATC) speech in English: 1) automatic speech-to-text transcription, 2) call sign detection (CSD). The registered participants were provided with 40 hours of speech along with manual transcriptions. Twenty-two teams submitted predictions on a five hour evaluation set. ATC speech processing is challenging for several reasons: high speech rate, foreign-accented speech with a great diversity of accents, noisy communication channels. The best ranked team achieved a 7.62% Word Error Rate and a 82.41% CSD F1-score. Transcribing pilots' speech was found to be twice as harder as controllers' speech. Remaining issues towards solving ATC ASR are also discussed.

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  1. ATCCaps: A Call-Sign-Aware Speech Dataset for Air Traffic Control Recognition

    cs.SD 2026-06 unverdicted novelty 6.0

    ATCCaps is a call-sign-aware ATC speech dataset containing 202.94 hours of audio, 170385 utterances and 922 unique call signs, constructed via transcript parsing, ADS-B metadata, normalization, filtering and LLM captioning.