Context-based recognition network adaptation for improving on-line ASR in Air Traffic Control

Schmidt, Anna Marie; Oualil, Youssef; Ohneiser, Oliver; Kleinert, Matthias; Schulder, Marc; Khan, Arif; Helmke, Hartmut; Klakow, Dietrich

doi:10.1109/slt.2014.7078542

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…Syntactic and semantic analyses [12,13] consist of parsing the result of recognized words from ASR systems and eliminating invalid sentences or words by respecting grammatical rules highly inspired by ICAO standard phraseology. It helps correct misrecognized out-of-vocabulary words with similar ones from valid words of the ATC vocabulary.…”

Section: Contextual Knowledge In Atcmentioning

confidence: 99%

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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Section: Contextual Knowledge In Atcmentioning

confidence: 99%

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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“…Various works have already investigated context incorporation in the ASR [5,6,7], which marks the prior step in the ATC speech processing pipeline. Two other works of the ATCO2 project [8,9] show that the combination of HCLG and lattice boosting using Kaldi [10], reduces the ATC-ASR errors, especially for the call-signs.…”

Section: Related Workmentioning

confidence: 99%

Call-Sign Recognition and Understanding for Noisy Air-Traffic Transcripts Using Surveillance Information

Blatt

Kocour

Veselý

et al. 2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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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 callsign recognition and understanding (CRU) system that addresses this issue. The recognizer is trained to identify callsigns 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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“…The knowledge of the dynamic contextual information was extracted by the ATC grammars which were specified by the International Civil Aviation Organization (ICAO). In [30], the contextual information is generated from a planning system, in which a grammar WFST based approach was further proposed to improve the ASR performance. The ASR hypothesis was also updated by a weighted Levenshtein distance of all possible words that are produced by an additional sequence labeling system [31].…”

Section: Atc Related Workmentioning

confidence: 99%

A Context-Aware Language Model to Improve the Speech Recognition in Air Traffic Control

et al. 2021

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Recognizing isolated digits of the flight callsign is an important and challenging task for automatic speech recognition (ASR) in air traffic control (ATC). Fortunately, the flight callsign is a kind of prior ATC knowledge and is available from dynamic contextual information. In this work, we attempt to utilize this prior knowledge to improve the performance of the callsign identification by integrating it into the language model (LM). The proposed approach is named context-aware language model (CALM), which can be applied for both the ASR decoding and rescoring phase. The proposed model is implemented with an encoder–decoder architecture, in which an extra context encoder is proposed to consider the contextual information. A shared embedding layer is designed to capture the correlations between the ASR text and contextual information. The context attention is introduced to learn discriminative representations to support the decoder module. Finally, the proposed approach is validated with an end-to-end ASR model on a multilingual real-world corpus (ATCSpeech). Experimental results demonstrate that the proposed CALM outperforms other baselines for both the ASR and callsign identification task, and can be practically migrated to a real-time environment.

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Context-based recognition network adaptation for improving on-line ASR in Air Traffic Control

Cited by 9 publications

References 3 publications

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

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

Call-Sign Recognition and Understanding for Noisy Air-Traffic Transcripts Using Surveillance Information

A Context-Aware Language Model to Improve the Speech Recognition in Air Traffic Control

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