Depression analysis of voice samples using machine learning

Abstract

Depression is seen as an emerging mental challenge in the lives of various people. Nowadays it is also becoming one of the major reasons for mental disability across the world. Depression has manifested itself as a silent killer and according to statistics it has affected more than 300 million people in United States of America majorly affecting individuals in the age group of 15 to 44 yrs. According to a study by World Health Organization, the effects of depression have been dangerous in life, it is seen causing threatening diseases like cancer, diabetic issues or even heart disease. However, the problem that mainly is associated with the disease of depression is that it is not treated as a disease. Where the common understanding of the word “Disease” is any medical ailment that require doctor’s attention or quick medical response, depression on the other hand, even after qualifying as a disease is hidden in societal barriers to appear for a proper treatment. People whose lifestyle pattern has been intruded by depression either do not avail proper medical attention or are too shy to appear in the masses for proper attention on their physical as well as condition. Our motivation here is to investigate through the phenomenon of depression and predict whether an individual is having symptoms of depression by accessing his/her voice sample. In order to establish a link between depression and voice features, we obtain a large data set and then train a model accordingly by applying machine learning methods on it. This model when given a voice sample can now predict, whether a particular subject is depressed or not, to a nearby accurate measure.