A High-Efficiency Fatigued Speech Feature Selection Method for Air Traffic Controllers Based on Improved Compressed Sensing

Air traffic controller fatigue has recently received considerable attention from researchers because it is one of the main causes of air traffic incidents. Numerous research studies have been conducted to extract speech features related to fatigue, and their practical utilization has achieved some positive detection results. However, there are still challenges associated with the applied speech features usually being of high dimension, which leads to computational complexity and inefficient fatigue detection. This situation makes it meaningful to reduce the dimensionality and select only a few efficient features. This paper addresses these problems by proposing a high-efficiency fatigued speech selection method based on improved compressed sensing. For adapting a method to the specific field of fatigued speech, we propose an improved compressed sensing construction algorithm to decrease the reconstruction error and achieve superior sparse coding. The proposed feature selection method is then applied to optimize the high-dimension fatigued speech features based on the fractal dimension. Finally, a support vector machine classifier is applied to a series of comparative experiments using the Civil Aviation Administration of China radiotelephony corpus to demonstrate that the proposed method provides a significant improvement in the precision of fatigue detection compared with current state-of-the-art approaches.