A Two-Step Approach to Leverage Contextual Data: Speech Recognition in Air-Traffic Communications
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU144938" target="_blank" >RIV/00216305:26230/22:PU144938 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9746563" target="_blank" >https://ieeexplore.ieee.org/document/9746563</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP43922.2022.9746563" target="_blank" >10.1109/ICASSP43922.2022.9746563</a>
Alternative languages
Result language
angličtina
Original language name
A Two-Step Approach to Leverage Contextual Data: Speech Recognition in Air-Traffic Communications
Original language description
Automatic Speech Recognition (ASR), as the assistance of speech communication between pilots and air-traffic controllers, can significantly reduce the complexity of the task and increase the reliability of transmitted information. ASR application can lead to a lower number of incidents caused by misunderstanding and improve air traffic management (ATM) efficiency. Evidently, high accuracy predictions, especially, of key information, i.e., callsigns and commands, are required to minimize the risk of errors. We prove that combining the benefits of ASR and Natural Language Processing (NLP) methods to make use of surveillance data (i.e. additional modality) helps to considerably improve the recognition of callsigns (named entity). In this paper, we investigate a two-step callsign boosting approach: (1) at the 1st step (ASR), weights of probable callsign n-grams are reduced in G.fst and/or in the decoding FST (lattices), (2) at the 2nd step (NLP), callsigns extracted from the improved recognition outputs with Named Entity Recognition (NER) are correlated with the surveillance data to select the most suitable one. Boosting callsign n-grams with the combination of ASR and NLP methods eventually leads up to 53.7% of an absolute, or 60.4% of a relative, improvement in callsign recognition.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISBN
978-1-6654-0540-9
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
6282-6286
Publisher name
IEEE Signal Processing Society
Place of publication
Singapore
Event location
Singapore
Event date
May 22, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000864187906114