Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Prasad, Amrutha"'
Autor:
Zuluaga-Gomez, Juan, Nigmatulina, Iuliia, Prasad, Amrutha, Motlicek, Petr, Khalil, Driss, Madikeri, Srikanth, Tart, Allan, Szoke, Igor, Lenders, Vincent, Rigault, Mickael, Choukri, Khalid
Voice communication between air traffic controllers (ATCos) and pilots is critical for ensuring safe and efficient air traffic control (ATC). This task requires high levels of awareness from ATCos and can be tedious and error-prone. Recent attempts h
Externí odkaz:
http://arxiv.org/abs/2305.01155
Autor:
Zuluaga-Gomez, Juan, Prasad, Amrutha, Nigmatulina, Iuliia, Motlicek, Petr, Kleinert, Matthias
In this paper we propose a novel virtual simulation-pilot engine for speeding up air traffic controller (ATCo) training by integrating different state-of-the-art artificial intelligence (AI) based tools. The virtual simulation-pilot engine receives s
Externí odkaz:
http://arxiv.org/abs/2304.07842
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
Autor:
Zuluaga-Gomez, Juan, Prasad, Amrutha, Nigmatulina, Iuliia, Sarfjoo, Saeed, Motlicek, Petr, Kleinert, Matthias, Helmke, Hartmut, Ohneiser, Oliver, Zhan, Qingran
Recent work on self-supervised pre-training focus on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E) acoustic models (AM) that can be later fine-tuned on downstream tasks e.g., automatic speech recognition (ASR). Yet, fe
Externí odkaz:
http://arxiv.org/abs/2203.16822
Autor:
Nigmatulina, Iuliia, Zuluaga-Gomez, Juan, Prasad, Amrutha, Sarfjoo, Seyyed Saeed, Motlicek, Petr
Publikováno v:
ICASSP 2022
Automatic Speech Recognition (ASR), as the assistance of speech communication between pilots and air-traffic controllers, can significantly reduce the complexity of the task and increase the reliability of transmitted information. ASR application can
Externí odkaz:
http://arxiv.org/abs/2202.03725
Autor:
Zuluaga-Gomez, Juan, Sarfjoo, Seyyed Saeed, Prasad, Amrutha, Nigmatulina, Iuliia, Motlicek, Petr, Ondrej, Karel, Ohneiser, Oliver, Helmke, Hartmut
Automatic speech recognition (ASR) allows transcribing the communications between air traffic controllers (ATCOs) and aircraft pilots. The transcriptions are used later to extract ATC named entities, e.g., aircraft callsigns. One common challenge is
Externí odkaz:
http://arxiv.org/abs/2110.05781
Autor:
Prasad, Amrutha, Zuluaga-Gomez, Juan, Motlicek, Petr, Sarfjoo, Saeed, Nigmatulina, Iuliia, Ohneiser, Oliver, Helmke, Hartmut
Automatic Speech Recognition (ASR) for air traffic control is generally trained by pooling Air Traffic Controller (ATCO) and pilot data into one set. This is motivated by the fact that pilot's voice communications are more scarce than ATCOs. Due to t
Externí odkaz:
http://arxiv.org/abs/2108.12175
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
State-of-the-art hybrid automatic speech recognition (ASR) system exploits deep neural network (DNN) based acoustic models (AM) trained with Lattice Free-Maximum Mutual Information (LF-MMI) criterion and n-gram language models. The AMs typically have
Externí odkaz:
http://arxiv.org/abs/2006.09054