This paper describes the creation of the AIRBUS-ATC corpus, which is a real-life, French-accented speech corpus of Air Traffic Control (ATC) communications (message exchanged between pilots and controllers) intended to build a robust ATC speech recognition engine. The corpus is currently composed of 59 hours of transcribed English audio, along with linguistic and meta-data annotations. It is intended to reach 100 hours by the end of the project. We describe ATC speech specificities, how the audio is collected, transcribed and what techniques were used to ensure transcription quality while limiting transcription costs. A detailed description of the corpus content (speaker gender, accent, role, type of control, speech turn duration) is given. Finally, preliminary results obtained with state-of-the-art speech recognition techniques support the idea that accent-specific corpora will play a pivotal role in building robust ATC speech recognition applications.
@InProceedings{DELPECH18.108, author = {Estelle Delpech and Marion Laignelet and Christophe Pimm and Céline Raynal and Michal Trzos and Alexandre Arnold and Dominique Pronto}, title = "{A Real-life, French-accented Corpus of Air Traffic Control Communications}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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