What You Say or How You Say It? Depression Detection Through Joint Modeling of Linguistic and Acoustic Aspects of Speech

Abstract Depression is one of the most common mental health issues. (It affects more than 4% world’s population, according to recent estimates.) This article shows that joint analysis linguistic and acoustic aspects speech allows discriminate between depressed nondepressed speakers with an accuracy above 80%. The approach used in work based on networks designed for sequence modeling (bidirectional Long-Short Term Memory networks) multimodal methodologies (late fusion, representation gated units). experiments were performed over a corpus 59 interviews (roughly 4 hours material) involving 29 individuals diagnosed depression 30 control participants. In addition 80%, results show approaches perform better unimodal ones owing people’s tendency manifest their condition through modality only, source diversity across approaches. addition, it possible measure “confidence” automatically identify subset test data which performance predefined threshold. It effectively detect by using unobtrusive inexpensive technologies automatic language.