ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Detecting English Speech in the Air Traffic Control Voice Communication

Igor Szöke, Santosh Kesiraju, Ondřej Novotný, Martin Kocour, Karel Veselý, Jan Černocký

Developing in-cockpit voice enabled applications require a real-world 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.

doi: 10.21437/Interspeech.2021-1033

Cite as: Szöke, I., Kesiraju, S., Novotný, O., Kocour, M., Veselý, K., Černocký, J. (2021) Detecting English Speech in the Air Traffic Control Voice Communication. Proc. Interspeech 2021, 3286-3290, doi: 10.21437/Interspeech.2021-1033

  author={Igor Szöke 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={Proc. Interspeech 2021},