Contextual adaptation of ASR can be very beneficial for multi-accent
and often noisy Air-Traffic Control (ATC) speech. Our focus is call-sign
recognition, which can be used to track conversations of ATC operators
with individual airplanes. We developed a two-stage boosting strategy,
consisting of HCLG boosting and Lattice boosting. Both are implemented
as WFST compositions and the contextual information is specific to
each utterance. In HCLG boosting we give score discounts to individual
words, while in Lattice boosting the score discounts are given to word
sequences. The context data have origin in surveillance database of
OpenSky Network. From this, we obtain lists of call-signs that are
made more likely to appear in the best hypothesis of ASR. This also
improves the accuracy of the NLU module that recognizes the call-signs
from the best hypothesis of ASR.
As part of ATCO2
project, we collected liveatc test set2. The boosting of call-signs
leads to 4.7% absolute WER improvement and 27.1% absolute increase
of Call-Sign recognition Accuracy (CSA). Our best result of 82.9% CSA
is quite good, given that the data is noisy, and WER 28.4% is relatively
high. We believe there is still room for improvement.
Cite as: Kocour, M., Veselý, K., Blatt, A., Gomez, J.Z., Szöke, I., Černocký, J., Klakow, D., Motlicek, P. (2021) Boosting of Contextual Information in ASR for Air-Traffic Call-Sign Recognition. Proc. Interspeech 2021, 3301-3305, doi: 10.21437/Interspeech.2021-1619
@inproceedings{kocour21_interspeech,
author={Martin Kocour and Karel Veselý and Alexander Blatt and Juan Zuluaga Gomez and Igor Szöke and Jan Černocký and Dietrich Klakow and Petr Motlicek},
title={{Boosting of Contextual Information in ASR for Air-Traffic Call-Sign Recognition}},
year=2021,
booktitle={Proc. Interspeech 2021},
pages={3301--3305},
doi={10.21437/Interspeech.2021-1619}
}