Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B Data
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 we...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/11/1/32 |
| _version_ | 1797344867690479616 |
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| author | Mohammed Saïd Kasttet Abdelouahid Lyhyaoui Douae Zbakh Adil Aramja Abderazzek Kachkari |
| author_facet | Mohammed Saïd Kasttet Abdelouahid Lyhyaoui Douae Zbakh Adil Aramja Abderazzek Kachkari |
| author_sort | Mohammed Saïd Kasttet |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T11:09:08Z |
| format | Article |
| id | doaj.art-22e4acc75e5e4255a5c35dcca6076cf9 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-08T11:09:08Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-22e4acc75e5e4255a5c35dcca6076cf92024-01-26T14:11:57ZengMDPI AGAerospace2226-43102023-12-011113210.3390/aerospace11010032Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B DataMohammed Saïd Kasttet0Abdelouahid Lyhyaoui1Douae Zbakh2Adil Aramja3Abderazzek Kachkari4Laboratory of Innovative Technologies (LTI), National Schools of Applied Sciences of Tangier (ENSAT), Tangier 90063, MoroccoLaboratory of Innovative Technologies (LTI), National Schools of Applied Sciences of Tangier (ENSAT), Tangier 90063, MoroccoLaboratory of Innovative Technologies (LTI), National Schools of Applied Sciences of Tangier (ENSAT), Tangier 90063, MoroccoLaboratory of Innovative Technologies (LTI), National Schools of Applied Sciences of Tangier (ENSAT), Tangier 90063, MoroccoThe Moroccan Airports Authority (ONDA) Tangier-Ibn Battouta Intl. Airport, Tangier 90032, MoroccoRecently, 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.https://www.mdpi.com/2226-4310/11/1/32ATCASRHMMDNNRNNWER |
| spellingShingle | Mohammed Saïd Kasttet Abdelouahid Lyhyaoui Douae Zbakh Adil Aramja Abderazzek Kachkari Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B Data Aerospace ATC ASR HMM DNN RNN WER |
| title | Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B Data |
| title_full | Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B Data |
| title_fullStr | Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B Data |
| title_full_unstemmed | Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B Data |
| title_short | Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B Data |
| title_sort | toward effective aircraft call sign detection using fuzzy string matching between asr and ads b data |
| topic | ATC ASR HMM DNN RNN WER |
| url | https://www.mdpi.com/2226-4310/11/1/32 |
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