Automatic Speech Recognition and Understanding in Air Traffic Management
Automatic Speech Recognition and Understanding (ASRU) has the potential to reduce air traffic controllers’ (ATCos) workload and to enhance air traffic management (ATM) safety. Automatic Speech Recognition (ASR) transforms voice signals into a sequence of words, e.g., “speed bird four one six descend...
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| Format: | Online |
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| Language: | English |
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MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Online Access: | ONIX_20240514_9783725803163_258 |
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| collection | Directory of Open Access Books |
| description | Automatic Speech Recognition and Understanding (ASRU) has the potential to reduce air traffic controllers’ (ATCos) workload and to enhance air traffic management (ATM) safety. Automatic Speech Recognition (ASR) transforms voice signals into a sequence of words, e.g., “speed bird four one six descend flight level one two zero”. Automatic Speech Understanding extracts the meaning from this word sequence, e.g., that the aircraft with the callsign “BAW416” should “DESCEND” to roughly “twelve thousand feet”. The Special Issue contains 12 articles authored by 54 different authors, working for 23 institutions that are located in 13 countries on four continents. These articles discuss (a) ontologies for modeling words and semantics, (b) the extraction of aircraft callsigns and complex commands, (c) ASRU support for ATCos through callsign highlighting, the filling of aircraft radar labels and flight strips for approach, tower, and apron environments, (d) supporting simulation pilots, (e) speech activity detection, speaker role classification, natural language processing, English language identification, (f) combining ASRU with surveillance data, (g) joining speech and gaze data, and (h) a safety assessment. |
| format | Online |
| id | doab-20.500.12854ir-137660 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1376602024-05-14T14:02:21Z Automatic Speech Recognition and Understanding in Air Traffic Management Helmke, Hartmut Ohneiser, Oliver speech recognition human–computer interaction situational awareness air traffic management air traffic controller flight callsign ASR VRS air traffic controller training simulation-pilot agent BERT automatic speech recognition and understanding speech synthesis automatic speech recognition natural language understanding semantic interpretation air traffic control radio communications intent representation semantic ontology performance metrics automatic speech understanding radar label human factors assistant system human-in-the-loop simulation multiple remote tower assistant-based speech recognition electronic flight strips simulation pilot workload apron control STARFiSH voice-driven control acoustic model grammar network syntax analysis semantic analysis unmanned aerial vehicle (UAV) UAV control speaker clustering speaker role detection air traffic control communications OpenSky Network callsign recognition ADS-B data safety assessment en-route sector approach sector fatigue recognition feature fusion multi-mode ATC HMM DNN RNN WER VHF ADS-B METAR GMTT speech corpus deep speech call sign detection levenshtein distance fuzzy string matching thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBF Social and ethical issues Automatic Speech Recognition and Understanding (ASRU) has the potential to reduce air traffic controllers’ (ATCos) workload and to enhance air traffic management (ATM) safety. Automatic Speech Recognition (ASR) transforms voice signals into a sequence of words, e.g., “speed bird four one six descend flight level one two zero”. Automatic Speech Understanding extracts the meaning from this word sequence, e.g., that the aircraft with the callsign “BAW416” should “DESCEND” to roughly “twelve thousand feet”. The Special Issue contains 12 articles authored by 54 different authors, working for 23 institutions that are located in 13 countries on four continents. These articles discuss (a) ontologies for modeling words and semantics, (b) the extraction of aircraft callsigns and complex commands, (c) ASRU support for ATCos through callsign highlighting, the filling of aircraft radar labels and flight strips for approach, tower, and apron environments, (d) supporting simulation pilots, (e) speech activity detection, speaker role classification, natural language processing, English language identification, (f) combining ASRU with surveillance data, (g) joining speech and gaze data, and (h) a safety assessment. 2024-05-14T14:02:16Z 2024-05-14T14:02:16Z 2024 book ONIX_20240514_9783725803163_258 9783725803163 9783725803156 https://directory.doabooks.org/handle/20.500.12854/137660 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/8877 https://mdpi.com/books/pdfview/book/8877 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0315-6 10.3390/books978-3-7258-0315-6 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725803163 9783725803156 318 open access |
| spellingShingle | speech recognition human–computer interaction situational awareness air traffic management air traffic controller flight callsign ASR VRS air traffic controller training simulation-pilot agent BERT automatic speech recognition and understanding speech synthesis automatic speech recognition natural language understanding semantic interpretation air traffic control radio communications intent representation semantic ontology performance metrics automatic speech understanding radar label human factors assistant system human-in-the-loop simulation multiple remote tower assistant-based speech recognition electronic flight strips simulation pilot workload apron control STARFiSH voice-driven control acoustic model grammar network syntax analysis semantic analysis unmanned aerial vehicle (UAV) UAV control speaker clustering speaker role detection air traffic control communications OpenSky Network callsign recognition ADS-B data safety assessment en-route sector approach sector fatigue recognition feature fusion multi-mode ATC HMM DNN RNN WER VHF ADS-B METAR GMTT speech corpus deep speech call sign detection levenshtein distance fuzzy string matching thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBF Social and ethical issues Automatic Speech Recognition and Understanding in Air Traffic Management |
| title | Automatic Speech Recognition and Understanding in Air Traffic Management |
| title_full | Automatic Speech Recognition and Understanding in Air Traffic Management |
| title_fullStr | Automatic Speech Recognition and Understanding in Air Traffic Management |
| title_full_unstemmed | Automatic Speech Recognition and Understanding in Air Traffic Management |
| title_short | Automatic Speech Recognition and Understanding in Air Traffic Management |
| title_sort | automatic speech recognition and understanding in air traffic management |
| topic | speech recognition human–computer interaction situational awareness air traffic management air traffic controller flight callsign ASR VRS air traffic controller training simulation-pilot agent BERT automatic speech recognition and understanding speech synthesis automatic speech recognition natural language understanding semantic interpretation air traffic control radio communications intent representation semantic ontology performance metrics automatic speech understanding radar label human factors assistant system human-in-the-loop simulation multiple remote tower assistant-based speech recognition electronic flight strips simulation pilot workload apron control STARFiSH voice-driven control acoustic model grammar network syntax analysis semantic analysis unmanned aerial vehicle (UAV) UAV control speaker clustering speaker role detection air traffic control communications OpenSky Network callsign recognition ADS-B data safety assessment en-route sector approach sector fatigue recognition feature fusion multi-mode ATC HMM DNN RNN WER VHF ADS-B METAR GMTT speech corpus deep speech call sign detection levenshtein distance fuzzy string matching thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBF Social and ethical issues |
| topic_facet | speech recognition human–computer interaction situational awareness air traffic management air traffic controller flight callsign ASR VRS air traffic controller training simulation-pilot agent BERT automatic speech recognition and understanding speech synthesis automatic speech recognition natural language understanding semantic interpretation air traffic control radio communications intent representation semantic ontology performance metrics automatic speech understanding radar label human factors assistant system human-in-the-loop simulation multiple remote tower assistant-based speech recognition electronic flight strips simulation pilot workload apron control STARFiSH voice-driven control acoustic model grammar network syntax analysis semantic analysis unmanned aerial vehicle (UAV) UAV control speaker clustering speaker role detection air traffic control communications OpenSky Network callsign recognition ADS-B data safety assessment en-route sector approach sector fatigue recognition feature fusion multi-mode ATC HMM DNN RNN WER VHF ADS-B METAR GMTT speech corpus deep speech call sign detection levenshtein distance fuzzy string matching thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBF Social and ethical issues |
| url | ONIX_20240514_9783725803163_258 |