Result Details

Boosting of Contextual Information in ASR for Air-Traffic Call-Sign Recognition

KOCOUR, M.; VESELÝ, K.; BLATT, A.; ZULUAGA-GOMEZ, J.; SZŐKE, I.; ČERNOCKÝ, J.; KLAKOW, D.; MOTLÍČEK, P. Boosting of Contextual Information in ASR for Air-Traffic Call-Sign Recognition. In Proceedings Interspeech 2021. Proceedings of Interspeech. Brno: International Speech Communication Association, 2021. no. 8, p. 3301-3305. ISSN: 1990-9772.
Type
conference paper
Language
English
Authors
Kocour Martin, Ing., DCGM (FIT)
Veselý Karel, Ing., Ph.D., DCGM (FIT)
BLATT, A.
ZULUAGA-GOMEZ, J.
Szőke Igor, Ing., Ph.D., DCGM (FIT)
Černocký Jan, prof. Dr. Ing., DCGM (FIT)
KLAKOW, D.
Motlíček Petr, doc. Ing., Ph.D., DCGM (FIT)
Abstract

Contextual adaptation of ASR can be very beneficial for multiaccentand often noisy Air-Traffic Control (ATC) speech. Ourfocus is call-sign recognition, which can be used to track conversationsof ATC operators with individual airplanes. Wedeveloped a two-stage boosting strategy, consisting of HCLGboosting and Lattice boosting. Both are implemented as WFSTcompositions and the contextual information is specific to eachutterance. In HCLG boosting we give score discounts to individualwords, while in Lattice boosting the score discountsare given to word sequences. The context data have origin insurveillance database of OpenSky Network. From this, we obtainlists of call-signs that are made more likely to appear inthe best hypothesis of ASR. This also improves the accuracyof the NLU module that recognizes the call-signs from the besthypothesis of ASR.As part of ATCO2 project, we collected liveatc test set2.The boosting of call-signs leads to 4.7% absolute WER improvementand 27.1% absolute increase of Call-Sign recognitionAccuracy (CSA). Our best result of 82.9% CSA is quitegood, given that the data is noisy, and WER 28.4% is relativelyhigh. We believe there is still room for improvement.

Keywords

Air Traffic Control, Automatic Speech Recognition,Contextual Adaptation, Call-sign Recognition, Call-signDetection, OpenSky Network

URL
Published
2021
Pages
3301–3305
Journal
Proceedings of Interspeech, vol. 2021, no. 8, ISSN 1990-9772
Proceedings
Proceedings Interspeech 2021
Conference
Interspeech Conference
Publisher
International Speech Communication Association
Place
Brno
DOI
10.21437/Interspeech.2021-1619
UT WoS
000841879503079
EID Scopus
2-s2.0-85106084788
BibTeX 
@inproceedings{BUT175845,
  author="KOCOUR, M. and VESELÝ, K. and BLATT, A. and ZULUAGA-GOMEZ, J. and SZŐKE, I. and ČERNOCKÝ, J. and KLAKOW, D. and MOTLÍČEK, P.",
  title="Boosting of Contextual Information in ASR for Air-Traffic Call-Sign Recognition",
  booktitle="Proceedings Interspeech 2021",
  year="2021",
  journal="Proceedings of Interspeech",
  volume="2021",
  number="8",
  pages="3301--3305",
  publisher="International Speech Communication Association",
  address="Brno",
  doi="10.21437/Interspeech.2021-1619",
  issn="1990-9772",
  url="https://www.isca-speech.org/archive/interspeech_2021/kocour21_interspeech.html"
}
Files
Projects
Automatic collection and processing of voice data from air-traffic communications, EU, Horizon 2020, start: 2019-11-01, end: 2022-02-28, completed
HAAWAII - Highly Automated Air Traffic Controller Workstations with Artificial Intelligence Integration, EU, Horizon 2020, H2020-SESAR-2019-2, start: 2020-06-01, end: 2022-11-30, completed
Moderní metody zpracování, analýzy a zobrazování multimediálních a 3D dat, BUT, Vnitřní projekty VUT, FIT-S-20-6460, start: 2020-03-01, end: 2023-02-28, completed
Research groups
Speech Data Mining Research Group BUT Speech@FIT (RG SPEECH)
Departments
Department of Computer Graphics and Multimedia (DCGM)
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