Detection of depression in speech

Depression is a common mental disorder and one of the main causes of disability worldwide. Lacking objective depressive disorder assessment methods is the key reason that many depressive patients can´t be treated properly. Developments in affective sensing technology with a focus on acoustic features will potentially bring a change due to depressed patient´s slow, hesitating, monotonous voice as remarkable characteristics. Our motivation is to find out a speech feature set to detect, evaluate and even predict depression. For these goals, we investigate a large sample of 300 subjects (100 depressed patients, 100 healthy controls and 100 high-risk people) through comparative analysis and follow-up study. For examining the correlation between depression and speech, we extract features as many as possible according to previous research to create a large voice feature set. Then we employ some feature selection methods to eliminate irrelevant, redundant and noisy features to form a compact subset. To measure effectiveness of this new subset, we test it on our dataset with 300 subjects using several common classifiers and 10-fold cross-validation. Since we are collecting data currently, we have no result to report yet.