Helmke, Hartmut und Ohneiser, Oliver, eds. (2024) Automatic Speech Recognition and Understanding in Air Traffic Management. Aerospce. MDPI (Aerospace). doi: 10.3390/books978-3-7258-0315-6. ISBN ISBN 978-3-7258-0316-3 (Hbk).
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Offizielle URL: https://www.mdpi.com/books/reprint/8877-automatic-speech-recognition-and-understanding-in-air-traffic-management
Kurzfassung
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.
elib-URL des Eintrags: | https://elib.dlr.de/203128/ | ||||||||||||
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Dokumentart: | Lehr- oder Fachbuch | ||||||||||||
Zusätzliche Informationen: | This book is a reprint of the Special Issue Automatic Speech Recognition and Understanding in Air Traffic Management that was published in Aerospace | ||||||||||||
Titel: | Automatic Speech Recognition and Understanding in Air Traffic Management | ||||||||||||
Datum: | März 2024 | ||||||||||||
Referierte Publikation: | Ja | ||||||||||||
Open Access: | Ja | ||||||||||||
Gold Open Access: | Nein | ||||||||||||
In SCOPUS: | Nein | ||||||||||||
In ISI Web of Science: | Nein | ||||||||||||
DOI: | 10.3390/books978-3-7258-0315-6 | ||||||||||||
Seitenbereich: | Seiten 1-320 | ||||||||||||
Herausgeber: |
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Verlag: | MDPI (Aerospace) | ||||||||||||
Name der Reihe: | Aerospce | ||||||||||||
ISBN: | ISBN 978-3-7258-0316-3 (Hbk) | ||||||||||||
Status: | veröffentlicht | ||||||||||||
Stichwörter: | 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 recognition; automatic speech understanding; air traffic management; air traffic controller; radar label; human factors; assistant system; human-in-the-loop simulation; air traffic controller; multiple remote tower; assistant-based speech recognition; automatic speech recognition and understanding; electronic flight strips; air traffic controller; simulation pilot; workload; assistant-based speech recognition; automatic speech recognition and understanding; apron control; STARFiSH; speech recognition; 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; automatic speech recognition and understanding; OpenSky Network; callsign recognition; ADS-B data; safety assessment; air traffic control; automatic speech recognition; workload; situational awareness; en-route sector; approach sector; fatigue recognition; air traffic controller; feature fusion; multi-mode; ATC; ASR; HMM; DNN; RNN; WER; VHF; ADS-B; METAR; GMTT; speech corpus; deep speech; call sign detection; levenshtein distance; fuzzy string matching | ||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||
HGF - Programm: | Luftfahrt | ||||||||||||
HGF - Programmthema: | Luftverkehr und Auswirkungen | ||||||||||||
DLR - Schwerpunkt: | Luftfahrt | ||||||||||||
DLR - Forschungsgebiet: | L AI - Luftverkehr und Auswirkungen | ||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | L - Integrierte Flugführung | ||||||||||||
Standort: | Braunschweig | ||||||||||||
Institute & Einrichtungen: | Institut für Flugführung > Lotsenassistenz | ||||||||||||
Hinterlegt von: | Diederich, Kerstin | ||||||||||||
Hinterlegt am: | 24 Apr 2024 15:02 | ||||||||||||
Letzte Änderung: | 29 Apr 2024 08:54 |
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