## 1 Introduction  
As air traffic volume increases, air traffic controller (ATC) fatigue has become a major cause of air traffic accidents. This state makes the operating ability of the controller decline, which makes it easy for them to cause work errors and even lead to accident symptoms or accidents. According to data from the American Aviation Safety Voluntary Reporting System, there are 52,000 unsafe incidents caused by fatigue, accounting for 21% of operational errors. According to data from the British Aviation Safety Voluntary Reporting System, 13% of the operational errors were directly related to controller fatigue, and according to data from China's ATC Safety Reporting System, 18% of work errors are related to controller fatigue [1, 2]. It can be seen that nearly one-fifth of the unsafe events in the operation of civil aviation are related to the fatigue of controllers. For this reason, it has become a hot topic in recent years to detect the fatigue state of ATC quickly and accurately. Fatigue detection usually judges the fatigue state according to some features related to the tested person [3]. At present, fatigue state detection methods of air traffic controllers can be divided into subjective detection methods and objective detection methods, according to different implementation paths. The subjective test method is mainly in the form of filling in a fatigue scale by the tested personnel [4, 5, 6, 7, 8, 9, 10]. According to the score on the fatigue scale, the fatigue state can be judged. This method is easy to implement but has poor applicability. The objective detection method refers to the use of auxiliary tools to record the changesin some indicators related to the human body, or human factor engineering, in order to judge the fatigue state. [PERSON] et al. [4] evaluated the fatigue state of controllers under different shift scheduling modes through the analysis and research of EEG signals. The study found that when the EEG frequency of the controller decreased to a certain extent, it appeared to be \(\theta\) wave, the ATC experienced decreased alertness and weakened operational ability. [PERSON] et al. proposed a fatigue classification method based on eyelid closure using simulation tower control software and an eye tracker. [PERSON] [8] and others extracted six indicators, such as the rate of a saccade, the time span of fixation and the percentage of eye closure time (PERCLOS) as the discrimination index of fatigue. The disadvantage of this method is that an image acquisition device needs to be set in front of the subject's face to collect facial images. On the one hand, it will make the subject more prone to fatigue under psychological pressure. On the other hand, it needs to process the image, and the detection results will be delayed to a certain extent. In recent years, the fatigue detection method based on speech has become a research hotspot of fatigue detection methods because of its high detection accuracy and strong applicability [11; 12; 13]. Speech signal data from the controller can be directly obtained by embedding a sensor in the controller's radio communication device. In the process of speech signal processing, compared with the linear analysis method, the application of the speech nonlinear dynamics analysis method can more completely obtain the impact of fatigue on speech signaling. Specifically, the fatigue state detection method based on speech features will not have any impact on the normal work of controllers, and it can greatly save the detection cost. The most important point is that the fatigue state detection method based on speech features can effectively improve the accuracy of the detection results. At present, the research on the fatigue state detection method based on speech features mainly focuses on time domain and frequency domain features. The commonly used time domain features include pitch, short-time energy, short-time zero crossing rate, average amplitude, harmonic noise ratio, autocorrelation coefficient, etc.