Person
Juan Pablo Zuluaga Gomez
This person is no longer with EPFL
Related publications (7)
A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers
Petr Motlicek, Juan Pablo Zuluaga Gomez, Amrutha Prasad
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 spoken ...
MDPI2023Validating Automatic Speech Recognition and Understanding for Pre-Filling Radar Labels-Increasing Safety While Reducing Air Traffic Controllers' Workload
Automatic speech recognition and understanding (ASRU) for air traffic control (ATC) has been investigated in different ATC environments and applications. The objective of this study was to quantify the effect of ASRU support for air traffic controllers (AT ...
2023A Two-Step Approach To Leverage Contextual Data: Speech Recognition In Air-Traffic Communications
Petr Motlicek, Juan Pablo Zuluaga Gomez, Amrutha Prasad
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 lead ...
IEEE2022Bertraffic: Bert-Based Joint Speaker Role And Speaker Change Detection For Air Traffic Control Communications
Petr Motlicek, Juan Pablo Zuluaga Gomez, Amrutha Prasad
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 speech ...
IEEE2022How Does Pre-Trained Wav2Vec 2.0 Perform On Domain-Shifted Asr? An Extensive Benchmark On Air Traffic Control Communications
Petr Motlicek, Juan Pablo Zuluaga Gomez, Amrutha Prasad
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, few work ...
IEEE2022Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems
Petr Motlicek, Juan Pablo Zuluaga Gomez, Amrutha Prasad
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 in Aut ...
ISCA-INT SPEECH COMMUNICATION ASSOC2021Boosting of contextual information in ASR for air-traffic call-sign recognition
Petr Motlicek, Juan Pablo Zuluaga Gomez
Contextual adaptation of ASR can be very beneficial for multi-accent and often noisy Air-Traffic Control (ATC) speech. Our focus is call-sign recognition, which can be used to track conversations of ATC operators with individual airplanes. We developed a t ...
ISCA-INT SPEECH COMMUNICATION ASSOC2021