Speaker‘s emotional states are recognized from speech signal with Additive white Gaussian noise (AWGN). The influence of white noise on a typical emotion recogniztion system is studied. The emotion classifier is implemented with Gaussian mixture model (GMM). A Chinese speech emotion database is used for training and testing, which includes nine emotion classes (e.g. happiness, sadness, anger, surprise, fear, anxiety, hesitation, confidence and neutral state). Two speech enhancement algorithms are introduced for improved emotion classification. In the experiments, the Gaussian mixture model is trained on the clean speech data, while tested under AWGN with various signal to noise ratios (SNRs). The emotion class model and the dimension space model are both adopted for the evaluation of the emotion recognition system. Regarding the emotion class model, the nine emotion classes are classified. Considering the dimension space model, the arousal dimension and the valence dimension are classified into positive regions or negative regions. The experimental results show that the speech enhancement algorithms constantly improve the performance of our emotion recognition system under various SNRs, and the positive emotions are more likely to be miss-classified as negative emotions under white noise environment.

Drawing on the stressor–emotion model, the study examines the mechanisms of counterproductive work behavior (CWB) development: specifically (1) the direct effect of job stressor (bullying at work); (2) the moderation effect of the Dark Triad (DT) and job control (JC); and (3) the moderated moderation effect (DT x JC) on the job stressor–CWB link. Data were collected among 763 white- and blue-collar workers. The hypotheses were tested by means of the PROCESS method. As expected in the hypotheses, high job stressor was directly related to high CWB, and DT moderated (increased) the link. JC also moderated the job stressor–CWB link, but the moderation effect was in a direction opposite to expectations. High job control participants were more likely to report CWB when they reported a high level of the stressors. The moderated moderation effect was supported. JC increases the moderation effect of DT on the job stressor–CWB link. The highest level of CWB was observed when DT and JC were high. The findings provide further insight into processes leading to the development of CWB.