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Highly Automated Air Traffic Controller Workstations with Artificial Intelligence Integration

Project description

DEENESFRITPL

Speech-to-text solution in the air

Safety is the highest priority for the aviation industry. Continuous advances in technology make it easier to keep crews and passengers safe. One such development is automatic speech recognition technology that can reduce air-traffic controllers’ workload and minimise human error. However, voice recognition that turns speech to text remains limited due to difficulties in distinguishing controllers’ accents and deviations from standard terminology. The EU-funded HAAWAII project will study and develop a reliable, error-resilient and adaptable solution that automatically transcribes voice commands issues by both air-controllers and pilots. The project will use an extensive collection of data to develop a new set of models for complex environments of Icelandic en route and London TMA, focusing on strengthening speech recognition models.

Objective

Advanced automation support developed in Wave 1 of SESAR IR includes using of automatic speech recognition (ASR) to reduce the amount of manual data inputs by air-traffic controllers. Evaluation of controllers’ feedback has been subdued due to the limited recognition performance of the commercial of the shell ASR engines that were used, even in laboratory conditions. The reasons for the unsatisfactory conclusions include e.g. inability to distinguish controllers’ accents, deviations from standard phraseology and limited real-time recognition performance. Past exploratory research funded project MALORCA, however, has shown (on restricted use-cases) that satisfactory performance can be reached with novel data-driven machine learning approaches.
Based on the results of MALORCA HAAWAII project aims to research and develop a reliable, error resilient and adaptable solution to automatically transcribe voice commands issued by both air-traffic controllers and pilots. The project will build on very large collection of data, organized with a minimum expert effort to develop a new set of models for complex environments of Icelandic en-route and London TMA. HAAWAII aims to perform proof-of-concept trials in challenging environments, i.e. to be directly connected with real-life data from ops room. As pilot read-back error detection is the main application, HAAWAII aims to significantly enhance the validity of the speech recognition models. The proposed work goes far beyond the work planned for the Wave 2 IR programme and will improve both safety and reduce controllers’ workload. The digitization of controller and pilot voice utterances can be used for a wide variety of safety and performance related benefits including, but not limiting to pre-fill entries into electronic flight strips and CPDLC messages. Another application demonstrated during proof-of-concept will be to objectively estimate controllers’ workload utilising digitized voice recordings of the complex London TMA.

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Programme(s)

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Call for proposal

H2020-SESAR-2019-2

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Funding Scheme

RIA - Research and Innovation action

Coordinator

DEUTSCHES ZENTRUM FUR LUFT - UND RAUMFAHRT EV
Net EU contribution
€ 520 000,00
Address
LINDER HOHE
51147 Koln
Germany

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Region
Nordrhein-Westfalen Köln Köln, Kreisfreie Stadt
Activity type
Research Organisations
Links
Total cost
€ 520 000,00

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Last update: 23 June 2023

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Permalink: https://cordis.europa.eu/project/id/884287

European Union, 2024