Zobrazeno 1 - 10
of 96
pro vyhledávání: '"Vesely Karel"'
Autor:
Karafiát, Martin, Veselý, Karel, Szöke, Igor, Mošner, Ladislav, Beneš, Karel, Witkowski, Marcin, Barchi, Germán, Pepino, Leonardo
This paper describes the joint effort of Brno University of Technology (BUT), AGH University of Krakow and University of Buenos Aires on the development of Automatic Speech Recognition systems for the CHiME-7 Challenge. We train and evaluate various
Externí odkaz:
http://arxiv.org/abs/2310.11921
Autor:
Prasad, Amrutha, Zuluaga-Gomez, Juan, Motlicek, Petr, Sarfjoo, Saeed, Nigmatulina, Iuliia, Vesely, Karel
This paper describes a simple yet efficient repetition-based modular system for speeding up air-traffic controllers (ATCos) training. E.g., a human pilot is still required in EUROCONTROL's ESCAPE lite simulator (see https://www.eurocontrol.int/simula
Externí odkaz:
http://arxiv.org/abs/2212.07164
Autor:
Zuluaga-Gomez, Juan, Veselý, Karel, Szöke, Igor, Blatt, Alexander, Motlicek, Petr, Kocour, Martin, Rigault, Mickael, Choukri, Khalid, Prasad, Amrutha, Sarfjoo, Seyyed Saeed, Nigmatulina, Iuliia, Cevenini, Claudia, Kolčárek, Pavel, Tart, Allan, Černocký, Jan, Klakow, Dietrich
Personal assistants, automatic speech recognizers and dialogue understanding systems are becoming more critical in our interconnected digital world. A clear example is air traffic control (ATC) communications. ATC aims at guiding aircraft and control
Externí odkaz:
http://arxiv.org/abs/2211.04054
Air traffic control (ATC) relies on communication via speech between pilot and air-traffic controller (ATCO). The call-sign, as unique identifier for each flight, is used to address a specific pilot by the ATCO. Extracting the call-sign from the comm
Externí odkaz:
http://arxiv.org/abs/2204.06309
Autor:
Zuluaga-Gomez, Juan, Nigmatulina, Iuliia, Prasad, Amrutha, Motlicek, Petr, Veselý, Karel, Kocour, Martin, Szöke, Igor
Air traffic management and specifically air-traffic control (ATC) rely mostly on voice communications between Air Traffic Controllers (ATCos) and pilots. In most cases, these voice communications follow a well-defined grammar that could be leveraged
Externí odkaz:
http://arxiv.org/abs/2104.03643
Autor:
Szoke, Igor, Kesiraju, Santosh, Novotny, Ondrej, Kocour, Martin, Vesely, Karel, Cernocky, Jan "Honza"
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) syste
Externí odkaz:
http://arxiv.org/abs/2104.02332
Autor:
Kocour, Martin, Cámbara, Guillermo, Luque, Jordi, Bonet, David, Farrús, Mireia, Karafiát, Martin, Veselý, Karel, Ĉernocký, Jan ''Honza''
This paper describes joint effort of BUT and Telef\'onica Research on development of Automatic Speech Recognition systems for Albayzin 2020 Challenge. We compare approaches based on either hybrid or end-to-end models. In hybrid modelling, we explore
Externí odkaz:
http://arxiv.org/abs/2101.12729
Advances in Automatic Speech Recognition (ASR) over the last decade opened new areas of speech-based automation such as in Air-Traffic Control (ATC) environment. Currently, voice communication and data links communications are the only way of contact
Externí odkaz:
http://arxiv.org/abs/2006.10304
Autor:
Karafiát, Martin, Baskar, Murali Karthick, Szöke, Igor, Vydana, Hari Krishna, Veselý, Karel, Černocký, Jan "Honza''
The paper describes the BUT Automatic Speech Recognition (ASR) systems submitted for OpenSAT evaluations under two domain categories such as low resourced languages and public safety communications. The first was challenging due to lack of training d
Externí odkaz:
http://arxiv.org/abs/2001.11360
Autor:
Baskar, Murali Karthick, Karafiat, Martin, Burget, Lukas, Vesely, Karel, Grezl, Frantisek, Cernocky, Jan Honza
Training deep recurrent neural network (RNN) architectures is complicated due to the increased network complexity. This disrupts the learning of higher order abstracts using deep RNN. In case of feed-forward networks training deep structures is simpl
Externí odkaz:
http://arxiv.org/abs/1808.01916