%Aigaion2 BibTeX export from Idiap Publications
%Saturday 21 December 2024 07:28:01 PM

@INPROCEEDINGS{Motlicek_INTERSPEECH_2020,
         author = {Zuluaga-Gomez, Juan and Motlicek, Petr and Zhan, Qingran and Braun, Rudolf and Vesely, Karel},
       keywords = {Air traffic control, Automatic Speech Recognition, deep neural networks, Lattice-Free MMI, transfer learning},
       projects = {Idiap, EC H2020- ATCO2},
          month = oct,
          title = {Automatic Speech Recognition Benchmark for Air-Traffic Communications},
      booktitle = {Proc. Interspeech 2020},
           year = {2020},
          pages = {2297-2301},
            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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2020/Motlicek_INTERSPEECH_2020.pdf}
}