## 4 Discussion  
The present study aimed to develop a monitoring system that can conveniently and effectively assess the stress level of nuclear power plant operators in real time to avoid potential human error. Therefore, the speech-based stress recognition approach was adopted in this study instead of traditional subjective scales and physiological parameters. We first investigated whether the economical and easily applicable simulated nuclear power plant task is an effective tool for inducing high subjective stress levels without focusing on social-evaluative components. Heart activity is correlated with stress [22], and skin conductance increases when the individual is under stress [23] due to the increase of moisture on the skin surface that increases the flow of electricity. The significant increase of both physiological indicators of heart rate and skin conductance in the present study indicated that participants experience distinct stress during the mission. The results of these physiological indicators were consistent with those of the subjective questionnaire, in which participants also experienced relatively high negative feelings. These findings indicated that mental stress is successfully induced by the acute psychological stress task during the stimulated nuclear power plant mission. Stress responses were consistent with significant changes in electrodermal activity and heart rate response to the stressful task obtained in previous studies [24]. Negative effects of digital human-machine interface characteristics on human error, such as increased cognitive load, mode confusion, and loss of situation awareness, were identified in field investigations in nuclear power plants and interviews with operators [25]. Moreover, negative characteristics of nuclear power plants, including complex control systems and false alarms, place an excessive amount of burden on operators and increase their stress levels. Detecting the stress level of operators is necessary; however, the use of physiological indicators as main measures for stress in previous studies will restrain operators' movement and cause other problematic situations. Determining stress levels through speech is non-invasive, less obtrusive, and less expensive compared with other methods for measuring stress. Such stress monitoring systems can be developed for nuclear power plant operators. The Air Force Research Laboratory in the USA claimed that voice-based stress detection systems can efficiently measure the stress of officers to reduce their workload, improve their effectiveness and thus save lives [26]. Therefore, applying  
Figure 10: Comparative Analysis of Twenty Test Samples. intelligent speech interaction systems to nuclear power plants can detect the stress state of operators without disturbing their work. An analysis of the speech recordings showed that measurable and systematic voice changes occur over the duration of the experiment. The change of voice features and MFCCs according to different stress levels can be used in predicting the stress state. The BP neural network model was developed using recording data. Compared with previous studies that classify emotions under different stressful conditions, we aimed to predict the precise stress level of operators. As a result, the prediction accuracy of the model can reach approximately 95%. [PERSON] et al. [27] used the SVM classification algorithm, including PCA processing, and achieved a maximum accuracy of 90% in distinguishing stress with speech recognition. Hence, our developed neural network model can evaluate the stress state more accurately and reliably compared with previous machine learning-based classification models. The proposed model presents the following limitations that constrain its generalization. (1) The task we applied in the experiment was more of a psychological paradigm that focuses on human fundamental cognitive processes compared with real nuclear power missions.