  We launched a community platform for collecting the ATC speech world-wide in
the ATCO2 project. Filtering out unseen 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.
