在匿名语音上实现高精度压力检测，兼顾隐私与性能。
Less Stress, More Privacy: Stress Detection on Anonymized Speech of Air Traffic Controllers
- 用深度学习模型分析匿名化空管员语音中的压力特征。
- 在真实数据集上达到93.6%准确率，模拟数据集80.1%。
- 为受隐私限制的高安全场景提供可部署的检测方案。
空中交通管制（ATC）在高压环境下需多任务并行，易引发压力。压力检测对维持高安全标准至关重要。然而，处理ATC语音数据受限于隐私法规（如GDPR）。通过匿名化语音数据可满足合规要求。本文评估了多种用于匿名化空管员语音的压力检测架构。最佳模型在匿名版Speech Under Simulated and Actual Stress（SUSAS）数据集上达到93.6%的准确率，在自建匿名化ATC仿真数据集上达80.1%。结果表明，隐私保护不会成为构建高性能深度学习模型的障碍。
原文摘要 · Abstract (English)
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.
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