Application of Speech on Stress Recognition with Neural Network in Nuclear Power Plant

Human failures occur in nuclear power plants when operators are under acute stress. Therefore, an automatic stressed recognition system should be developed for nuclear power work. Previous studies on the prediction of stress are limited because of their reliance on subjective ratings and contact physiological measurement. To solve this problem, we developed a non-intrusive way by using voice features to detect stress. We aim to build a system that can estimate the level of stress from speech which may be applied to nuclear power plants where operators engage in regular verbal communication as part of their duties. In this study, we collected voice recordings from 34 participants during a simulated nuclear plant power task in a time-limited situation that requires high cognitive resources. Mel frequency cepstrum coefficients (MFCCs) were extracted from stressed voice samples and the neural network model was used to assess stress levels continuously. The experimental results showed that voice features can provide satisfactory predictions of the stress state. Mean relative errors of prediction are possible within approximately 5%. We discuss the implications of the use of voice as a minimally intrusive means for monitoring the effects of stress on cognitive performance in practical applications.