logo Idiap Research Institute        
 [BibTeX] [Marc21]
Automatic Speech Recognition Benchmark for Air-Traffic Communications
Type of publication: Conference paper
Citation: Motlicek_INTERSPEECH_2020
Publication status: Accepted
Booktitle: Proc. Interspeech 2020
Year: 2020
Month: October
Pages: 2297-2301
Crossref: http://dx.doi.org/10.21437/Interspeech.2020-2173
DOI: 10.21437/Interspeech.2020-2173
Abstract: Advances in Automatic Speech Recognition (ASR) over the last decade opened new areas of speech-based automation such as in Air-Traffic Control (ATC) environments. Currently, voice communication and Controller Pilot Data Link Communications are the only way of contact between pilots and Air-Traffic Controllers (ATCo), where the former is the most widely used and the latter is a non-speech method mandatory for oceanic messages and limited for some domestically issues. ASR systems on ATCo environments inherit increasing complexity due to accents from non-English speakers, cockpit noise, speaker-dependent biases and small in-domain ATC databases for training. In this paper, we review the last advances related to ASR on ATCo communication. Then, we introduce CleanSky EC H2020 ATCO2, a project that aims to develop a platform to collect, organize and automatically pre-process ATCo data from air space. We apply transfer learning from out-of-domain corpus coupled with adaptation on seven command-related corpora. The acoustic modelling is based on conventional TDNN-HMMs trained using lattice-free MMI objective function. The developed ASR achieves relative improvement in word error rates of 29% when using transfer learning and an additional 36% when adapting the model with seven command-related databases, these results obtained from EC H2020 SESAR project MALORCA Vienna database.
Keywords: Air traffic control, Automatic Speech Recognition, deep neural networks, Lattice-Free MMI, transfer learning
Projects Idiap
EC H2020- ATCO2
Authors Juan, Zuluaga-Gomez.
Motlicek, Petr
Zhan, Qingran
Braun, Rudolf
Vesely, Karel
Added by: [UNK]
Total mark: 0
  • Motlicek_INTERSPEECH_2020.pdf
       (Submitted Manuscript)