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

Blatt, Alexander; Kocour, Martin; Veselý, Karel; Szöke, Igor; Klakow, Dietrich

doi:10.48550/arxiv.2204.06309

Cited by 1 publication

(2 citation statements)

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“…Semantic analysis is the process of testing the meaning of sentences. It can help resolve ambiguity and recognize words despite background noises [14].…”

Section: Contextual Knowledge In Atcmentioning

confidence: 99%

“…The results showcased in [17,18] reported up to 60.4% relative improvement in call sign recognition by boosting call sign n-grams with the combination of ASR and NLP methods to use surveillance data. Finally, by leveraging surveillance information, Blatt, A et al [14] significantly improved the accuracy of call-sign recognition in noisy air traffic control environments. The model showed a 20% improvement compared to existing methods.…”

Section: Call Sign Detection (Csd)mentioning

confidence: 99%

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Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B Data

Kasttet,

Lyhyaoui,

Zbakh

et al. 2023

Aerospace

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Recently, artificial intelligence and data science have witnessed dramatic progress and rapid growth, especially Automatic Speech Recognition (ASR) technology based on Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). Consequently, new end-to-end Recurrent Neural Network (RNN) toolkits were developed with higher speed and accuracy that can often achieve a Word Error Rate (WER) below 10%. These toolkits can nowadays be deployed, for instance, within aircraft cockpits and Air Traffic Control (ATC) systems in order to identify aircraft and display recognized voice messages related to flight data, especially for airports not equipped with radar. Hence, the performance of air traffic controllers and pilots can ultimately be improved by reducing workload and stress and enforcing safety standards. Our experiment conducted at Tangier’s International Airport ATC aimed to build an ASR model that is able to recognize aircraft call signs in a fast and accurate way. The acoustic and linguistic models were trained on the Ibn Battouta Speech Corpus (IBSC), resulting in an unprecedented speech dataset with approved transcription that includes real weather aerodrome observation data and flight information with a call sign captured by an ADS-B receiver. All of these data were synchronized with voice recordings in a structured format. We calculated the WER to evaluate the model’s accuracy and compared different methods of dataset training for model building and adaptation. Despite the high interference in the VHF radio communication channel and fast-speaking conditions that increased the WER level to 20%, our standalone and low-cost ASR system with a trained RNN model, supported by the Deep Speech toolkit, was able to achieve call sign detection rate scores up to 96% in air traffic controller messages and 90% in pilot messages while displaying related flight information from ADS-B data using the Fuzzy string-matching algorithm.

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“…Semantic analysis is the process of testing the meaning of sentences. It can help resolve ambiguity and recognize words despite background noises [14].…”

Section: Contextual Knowledge In Atcmentioning

confidence: 99%