AI News, BOOK REVIEW: Speech recognition technology for air traffic controllers

Speech recognition technology for air traffic controllers

One of the greatest hurdles to introducing higher levels of automation in air traffic management (ATM) is the intensive use of voice radio communication to convey air traffic control (ATC) instructions to pilots.

The Horizon 2020 funded MALORCA project aimed to reduce the development and maintenance costs of assistant-based speech recognition (ABSR) by using machine learning instead of manual software programming.This initiative was funded within the framework of the SESAR Joint Undertaking, a public-private partnership set up to modernise Europe's ATM system.

Currently, several speech recognition modules require a manual adaptation to local needs caused by acoustic and language variabilities such as regional accents, phraseology deviations and local constraints.

Machine learning employs statistical techniques that enable computer systems to 'learn' and improve their performance on specific tasks over time by exploiting this data, without being explicitly programmed.

The first step Project partners used the output of a so-called 'Arrival Manager' for Prague and Vienna airports to automatically split the untranscribed training data into positive and negative chunks through specific confidence metrics.

The project thus provides the aviation industry with a practical approach for developing and deploying a state-of-the art speech recognition system and integrating it into today's voice communication systems for air navigation service providers.

MALORCA (Machine Learning of Speech Recognition Models for Controller Assistance)

The AcListant® project has 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.

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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.).

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.

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