Stress detection from speech and Galvanic Skin Response signals

Abstract

The problem of stress-management has been receiving an increasing attention in related research communities due to a wider recognition of potential problems caused by chronic stress and due to the recent developments of technologies providing non-intrusive ways of collecting continuously objective measurements to monitor person's stress level. Experimental studies have shown already that stress level can be judged based on the analysis of Galvanic Skin Response (GSR) and speech signals. In this paper we investigate how classification techniques can be used to automatically determine periods of acute stress relying on information contained in GSR and/or speech of a person.