Publication Details
Detecting English Speech in the Air Traffic Control Voice Communication
Kesiraju Santosh, Ph.D. (DCGM)
Novotný Ondřej, Ing., Ph.D.
Kocour Martin, Ing. (DCGM)
Veselý Karel, Ing., Ph.D. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
speech recognition, language detection, x-vector extractor, acoustic model,
air-traffic communication, data collection, text embeddings, Bayesian methods
Developing in-cockpit voice enabled applications require a realworld dataset with
labels and annotations. We launched a community platform for collecting the
Air-Traffic Control (ATC) speech, world-wide in the ATCO2 project. Filtering out
non- English speech is one of the main components in the data processing
pipeline. The proposed English Language Detection (ELD) system is based on the
embeddings from Bayesian subspace multinomial model. It is trained on the word
confusion network from an ASR system. It is robust, easy to train, and light
weighted. We achieved 0:0439 equal-error-rate (EER), a 50% relative reduction as
compared to the state-of-the-art acoustic ELD system based on x-vectors, in the
in-domain scenario. Further, we achieved an EER of 0:1352, a 33% relative
reduction as compared to the acoustic ELD, in the unseen language (out-of-domain)
condition. We plan to publish the evaluation dataset from the ATCO2 project.
@inproceedings{BUT175844,
author="Igor {Szőke} and Santosh {Kesiraju} and Ondřej {Novotný} and Martin {Kocour} and Karel {Veselý} and Jan {Černocký}",
title="Detecting English Speech in the Air Traffic Control Voice Communication",
booktitle="Proceedings Interspeech 2021",
year="2021",
journal="Proceedings of Interspeech",
volume="2021",
number="8",
pages="3286--3290",
publisher="International Speech Communication Association",
address="Brno",
doi="10.21437/Interspeech.2021-1033",
issn="1990-9772",
url="https://www.isca-speech.org/archive/interspeech_2021/szoke21_interspeech.html"
}