ARTICLE Ahrenhold_AEROSPACE_2023/IDIAP Validating Automatic Speech Recognition and Understanding for Pre-Filling Radar Labels-Increasing Safety While Reducing Air Traffic Controllers' Workload Ahrenhold, Nils Helmke, Hartmut Mühlhausen, Thorsten Ohneiser, Oliver Kleinert, Matthias Ehr, heiko Klamert, Lucas Juan, Zuluaga-Gomez. air traffic controller air traffic management assistant system Automatic Speech Recognition automatic speech understanding human factors human-in-the-loop simulation radar label EXTERNAL http://publications.idiap.ch/attachments/papers/2023/Ahrenhold_AEROSPACE_2023.pdf PUBLIC Aerospace 10 6 538 2226-4310 2023 https://doi.org/10.3390/aerospace10060538 doi 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 (ATCos) radar label maintenance in terms of safety and human performance. Therefore, an implemented ASRU system was validated within a human-in-the-loop environment by ATCos in different traffic-density scenarios. In the baseline condition, ATCos performed radar label maintenance by entering verbally instructed ATC commands with a mouse and keyboard. In the proposed solution, ATCos were supported by ASRU, which achieved a command recognition rate of 92.5% with a command error rate of 2.4%. ASRU support reduced the number of wrong or missing inputs from ATCos into the radar label by a factor of two, which contemporaneously improved their situational awareness. Furthermore, ATCos where able to perform more successful secondary tasks when using ASRU support, indicating a greater capacity to handle unexpected events. The results from NASA TLX showed that the perceived workload decreased with a statistical significance of 4.3% across all scenarios. In conclusion, this study provides evidence that using ASRU for radar label maintenance can significantly reduce workload and improve flight safety.