Stress Detection from Audio on Multiple Window Analysis Size in a Public Speaking Task

Speech production modifications are one of the many indications of stress in humans. A job interview simulation task permitted the collection of a multimodal corpus, including physiological data. Physiological cues of stress are reliable on long periods, and require invasive sensors. Human voice variations have been proved to be a non-invasive stress cue. In this paper, we focus on a frame-wise detection of stress on several window analysis sizes and analyze the behavior of different audio features classes. We trained our system on 19 subjects, and test it on 10 other subjects. Our best system obtains a 71.9 percent Unweighted Average Recall on 5s windows.