Detecting English Speech in the Air Traffic Control Voice Communication

Developing in-cockpit voice enabled applications require a realworlddataset with labels and annotations. We launched a communityplatform 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 processingpipeline. The proposed English Language Detection(ELD) system is based on the embeddings from Bayesian subspacemultinomial model. It is trained on the word confusionnetwork from an ASR system. It is robust, easy to train, andlight weighted. We achieved 0:0439 equal-error-rate (EER),a 50% relative reduction as compared to the state-of-the-artacoustic ELD system based on x-vectors, in the in-domain scenario.Further, we achieved an EER of 0:1352, a 33% relativereduction as compared to the acoustic ELD, in the unseen language(out-of-domain) condition. We plan to publish the evaluationdataset from the ATCO2 project.