Detecting Psychological Stress from Speech using Deep Neural Networks and Ensemble Classifiers

Speech stress detection remains an important research area, with applicability to fields and tasks such as remote monitoring, virtual assistance software, forensics operations, and even health and safety. This paper proposes a deep learning system, based on multiple Deep Neural Networks (DNNs) joined within an ensemble one-vs-one (OvO) classification strategy, using an extensive set of algorithmically extracted acoustic, prosodic, spectral, and cepstral features. The system was tested on the Speech Under Simulated and Actual Stress (SUSAS) database, for 5 class subsets and groups. Improvements have been obtained over previously reported results, with an unweighted accuracy (UA) between 62.4% and 76.1%, depending on the number of classes and their grouping.