Nowadays, air traffic control (ATC) instructions are usually still given via voice communication to the pilots. But ATC systems, to be safe and efficient, need up-to-date data. Therefore, it requires lots of inputs from the air traffic controllers (ATCOs) to keep the system data correct. Mouse and keyboard are mostly used for this purpose generating high workload for the ATCO.
Automatic speech recognition converts speech to text and is, therefore, an alternative input modality. The projects AcListant® and AcListant®-Strips have shown that Assistant Based Speech Recognition (ABSR) can significantly reduce controllers’ workload and increase ATM efficiency (fuel savings of 50 to 65 litres per flight).
One main issue to transfer ABSR from the laboratory to the operational systems are the costs of deployment, because modern speech recognition models require manual adaptation to local requirements (local accents, phraseology deviations, environmental constraints etc.). AcListant® needed e.g. 1.3 Mio € for development and validation for Düsseldorf approach area. Currently, modern models of speech recognition require manual adaptation to a local environment.
The Horizon 2020 SESAR project MALORCA (Machine Learning of Speech Recognition Models for Controller Assistance) is partly funded by SESAR Joint Undertaking (Grant Number 698824). MALORCA proposes a general, cheap and effective solution to automate this re-learning, adaptation and customisation process by automatically learning local speech recognition and controllers models from radar and speech data recordings
The German Aerospace Center (DLR), Saarland University (USAAR), Idiap Research Institute (Idiap), Austro Control Österreichische Gesellschaft für Zivilluftfahrt mit beschränkter Haftung (ACG), and Air Navigation Services of the Czech Republic (ANS CR) work together to automatically and more efficiently improve speech recognition models for assistance at different controller working positions.