Less Stress, More Privacy: Stress Detection on Anonymized Speech of Air Traffic Controllers
This addresses stress monitoring for air traffic controllers to enhance safety, but it is incremental as it applies existing deep learning methods to anonymized data.
The paper tackled stress detection in air traffic controllers' speech while complying with privacy regulations by anonymizing data, achieving 93.6% accuracy on an anonymized SUSAS dataset and 80.1% on an anonymized ATC simulation dataset.
Air traffic control (ATC) demands multi-tasking under time pressure with high consequences of an error. This can induce stress. Detecting stress is a key point in maintaining the high safety standards of ATC. However, processing ATC voice data entails privacy restrictions, e.g. the General Data Protection Regulation (GDPR) law. Anonymizing the ATC voice data is one way to comply with these restrictions. In this paper, different architectures for stress detection for anonymized ATCO speech are evaluated. Our best networks reach a stress detection accuracy of 93.6% on an anonymized version of the Speech Under Simulated and Actual Stress (SUSAS) dataset and an accuracy of 80.1% on our anonymized ATC simulation dataset. This shows that privacy does not have to be an impediment in building well-performing deep-learning-based models.