DETECTION OF DEPRESSION IN SPEECH MANAGEMENT SYSTEM PROJECT.

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.

In this project we are detecting depression from users post,user can upload post in the form of text file,image

file, or audio file,this project an help peoplewho are in depression by sending motivated messages to

them.Now-a-days peoples are using online post service to interact with each other compare to human to

human interaction.So by analysinguser post this application can detect depression and send motivation

messages to them.Administrator of this application will send motivation messages to all people who are

depression.To detect depression we are using SVM (support vector machine)algorithm which analyse users

post and give result as negative or positive.If users express depression words in post then SVM detect it as a

negative post else positive post.