## 1 Introduction
In addition, [PERSON] used ZigBee collected galvanic skin response (GSR) data to detect the stress state successfully [13]. Physiological parameters are regulated by the ANS and endocrine system and remain unaffected by subjective factors. In [PERSON]'s review, physiological parameters combined with machine learning techniques are used as objective indicators to assess the psychological stress state [14]. However, intrusive measuring devices that are used to collect physiological data may restrain operator movements and cause physical discomfort. The procedure for wearing such devices or equipment is complicated and requires significant ability. The extra mental and physical burden on operators caused by these devices can directly reduce the accuracy of stress detection. Therefore, the use of physiological signals in detecting the stress state is an inconvenient method in the nuclear power mission. Hence, a real-time and nonintrusive stress monitoring system is necessary in predicting stress from voice samples. This type of system can be useful in situations that require operators to communicate regularly by speaking. Studies have identified a number of vocal correlations with stress. According to [PERSON] and [PERSON] [15], the increase of the respiration rate of an individual will increase the fundamental frequency during the voiced section and lead to a shortened duration of speech between breaths in the event of stressful situations. Dryness of mouth during stress situations can also affect the muscle activity of the larynx and the condition of the vocal cords. Therefore, speculating that speech features can detect and predict the stress state of operators is logical. We extracted the mel frequency cepstrum coefficient (MFCC) feature for model construction in the present study. For MFCC, the main advantage is that it uses mel-frequency scaling, which is very similar to the human auditory system [16]. According to [PERSON], MFCC has been shown to be promising for describing stress [17]. Inducing acute stress in nuclear power plant operators during simulated scenarios is difficult. Many studies in field of psychology have induced stress using the Trier Social Stress Task (TSST), where participants give a speech and perform complex arithmetic in front of a stern panel of evaluators [18]. A social-evaluative threat is the key ingredient for situations that elicit stressful feelings in TSST. However, this type of threat is not the main cause of anxiety and exhaustion in nuclear power operators. According to [PERSON] et al. [19], mental arithmetic is a cognitive stressor that can effectively elicit cardiovascular responses and negative emotions. For nuclear power plant operators, the stress may be induced by a sudden fault or excessive workloads. In simulated scenarios, the mental stress may be induced more successfully and easily by increasing the workload compared with the creation of simulated faults. Therefore, we integrated mental arithmetic with short-time memory tasks into simulated nuclear power plant scenarios to elicit psychological stress and verified whether the induction was successful through objective physiological parameters and subjective self-reporting. A new method that uses speech as the stress identification index was proposed in this study in order to overcome the inapplicability of previous stress detection methods in simulated nuclear power plant scenarios. We built a predictive model of stress from speech recordings using a neural network that treats stress as a continuous measure based on the subjective rating of PANAS to provide the specific stress state information of operators. Our goal was to test whether speech can predict objective stress and estimate the possible performance of machine learning approaches and then provide nuclear power plants with a voice recognition method that can measure the stress state of operators in real time without interfering with their work.