Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Conferences, Workshops, Symposiums, and Seminars
  4. A Two-Step Approach To Leverage Contextual Data: Speech Recognition In Air-Traffic Communications
 
conference paper

A Two-Step Approach To Leverage Contextual Data: Speech Recognition In Air-Traffic Communications

Nigmatulina, Iuliia
•
Zuluaga-Gomez, Juan
•
Prasad, Amrutha
Show more
January 1, 2022
2022 Ieee International Conference On Acoustics, Speech And Signal Processing (Icassp)
47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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 1 st step (ASR), weights of probable callsign n-grams are reduced in G.fst and/or in the decoding EST (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.

  • Details
  • Metrics
Type
conference paper
DOI
10.1109/ICASSP43922.2022.9746563
Web of Science ID

WOS:000864187906114

Author(s)
Nigmatulina, Iuliia
Zuluaga-Gomez, Juan
Prasad, Amrutha
Sarfjoo, Seyyed Saeed
Motlicek, Petr  
Date Issued

2022-01-01

Publisher

IEEE

Publisher place

New York

Published in
2022 Ieee International Conference On Acoustics, Speech And Signal Processing (Icassp)
ISBN of the book

978-1-6654-0540-9

Series title/Series vol.

International Conference on Acoustics Speech and Signal Processing ICASSP

Start page

6282

End page

6286

Subjects

Acoustics

•

Computer Science, Artificial Intelligence

•

Engineering, Electrical & Electronic

•

Computer Science

•

Engineering

•

automatic speech recognition

•

human-computer interaction

•

air-traffic control

•

air-surveillance data

•

callsign detection

•

finite-state transducers

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LIDIAP  
Event nameEvent placeEvent date
47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Singapore, SINGAPORE

May 22-27, 2022

Available on Infoscience
January 16, 2023
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/193836
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés