## 1 Introduction
Frequency domain features include formant frequency, Mel frequency cepstrum coefficient (MFCC), linear prediction cepstrum coefficient (LPCC) and line spectrum pair (LSP) [14; 15]. In this paper, we chose the MFCC as the detection feature. Fatigue detection, in essence, can be attributed to a two-classification problem: normal/fatigue. At present, the main research direction in the field of fatigue detection is the machine learning method. Some typical machine learning methods, such as k-nearest neighbor (KNN), back propagation neural network (BPNN) and deep learning, have been successfully applied in this field [16; 17; 18; 19; 20; 21; 22; 23; 24]. However, for machine learning, in order to obtain an ideal detection model, an unavoidable problem is the parameter setting. Although many scholars have performed a lot of work on parameter selection, it is difficult to find the optimal parameters. A poor parameter may affect the detection effect and has become a bottleneck in the application of machine learning methods. In recent years, an ensemble learning algorithm has attracted more and more attention [25; 26]. The ensemble learning algorithm improves the generalization performance of the whole algorithm by integrating multiple base classifiers. In ensemble learning, in order to obtain a good generalization ability, the decision accuracy of each base classifier is required to be higher than that of a random guess. In this condition, the SVM, KNN, Bayesian and other methods with good generalization ability almost do not need to consider the parameter problem, thus avoiding parameter selection. In this way, the current bottleneck problem can be broken through. So, this paper chooses ensemble learning to research fatigue detection. The process of ensemble learning mainly includes two parts: the construction of a base classifier and the fusion method of multi-classifier results [27]. The greater the difference in each base classifier, the stronger the overall performance that can be obtained. In order to improve the differences between the base classifiers, this paper adopts the method of heterogeneous integration and multi-sampling. In addition, the fusion method of the base classifier is the other key problem affecting the detection model. At present, the most commonly used and effective methods are the voting method and the weighting method. In contrast, the weighted method is an improvement on the voting method. It weighs the decision-making output according to the classification performance of different base classifiers. However, how much weight is assigned and whether the result is optimal is another problem. In order to solve this problem, this paper uses a quantum genetic algorithm to optimize the weights of each base classifier and improve the accuracy of the detection model. To sum up, this paper takes the ensemble learning method as the core detection algorithm and improves the detection accuracy by optimizing the construction of the base classifier and result fusion methods. The specific arrangement of the paper is as follows:  
In the second section, the speech feature extraction method MFCC is introduced. The third section introduces the whole algorithm, heterogeneous integration and sampling are introduced to improve the difference in base classifiers, and then optimizes the weighted method of decision-making with SQGA; the fourth section tests the proposed algorithm on some basic datasets to verify the effectiveness of the algorithm; in the fifth section, this method is applied to ATC fatigue detection to test the performance of the algorithm; and finally, the full text is summarized.