空中交通管制员在执行管制任务中全程采用陆空通话标准用语与机组进行交流，通过对管制员陆空通话语音进行疲劳检测，可以有效地评估管制员的疲劳状态，减小飞行安全风险。鉴于管制员疲劳状态难以实时标记且存在主观性的问题，提出了基于无监督学习的管制员疲劳状态识别模型。首先，利用管制员模拟机语音数据探究疲劳状态在语音特征中的呈现情况，通过标准化处理和特征递归消除交叉验证方法（recursive feature elimination cross validation, REFCV）从62维GeMAPS语音特征中筛选出8个具有较强疲劳识别能力的特征，并对比在疲劳状态和正常状态下的特征值大小。其次，利用所筛选特征对管制员疲劳状态进行有监督分类，识别准确率达到82.87%，证明了所筛选特征的疲劳识别能力。最后，将管制语音数据进行聚类，根据特征趋势分析聚类簇分布，重新划分疲劳状态，并对聚类效果进行了验证，得到98.78%的疲劳状态检测准确率。研究成果为后续运用真实工作场景下的管制语音数据进行疲劳状态研究提供了理论基础和实验支撑。

Air traffic controllers communicate with flight crews by using standard language for radiotelephony communication throughout the execution of control tasks, and fatigue detection of controllers' radiotelephony communication voice can effectively assess the fatigue state of controllers and reduce the flight safety risk. Given that controller fatigue state is difficult to mark in real time and it is subjective, a controller fatigue state recognition model based on unsupervised learning was proposed. Firstly, the controller simulator speech data were used to investigate the presentation of fatigue state in speech features, and eight features with strong fatigue recognition ability were screened from 62-dimensional GeMAPS speech features by normalisation and recursive feature elimination cross validation (REFCV), and the feature values were compared between fatigue state and normal state. Secondly, the supervised classification of controller fatigue state was performed using the selected features, and the recognition accuracy reaches 82.87%, which proves the fatigue recognition ability of the selected features. Finally, the control voice data were clustered, and the cluster distribution was analysed according to the feature trend to reclassify the fatigue state, and the clustering effect was verified, obtaining a fatigue state detection accuracy of 98.78%. The research results provide a theoretical basis and experimental support for the subsequent fatigue state research using control speech data in real work scenarios.