Clinical Depression Detection Using Speech Feature With Machine Learning Approach

Depression is a general mental health disorder that presents state of low mood, negative thoughts, mental disturbance, typically with lack of energy , difficulty in maintaining concentration, guilty, irritable, restless and cognitive difficulties such as lose interest in different new things. Clinical depression is a major risk factor in suicides and is associated with high mortality rates, therefore making it one of the leading causes of death worldwide every year. The landmark World Health organisation(WHO) Global Burden of Disease (GBD) quantified depression as the second highest leading cause of disability world-wide[1]. It is observed that, there is increase in tendency of clinical depression in adolescents (i.e. age between 13'“20 years) has been linked to a range of serious problem, basically an increase in the number of suicide attempts and deaths. This is making public health concern. In this project we are detecting whether the person is in depression or not using tensor flow software. There various biomarkers of depression like facial expressions, speech, pupil, T-body shape, MRI, EEG, etc. Here we are processing on speech feature extracted from database by SVM technique. Again among features of speech like TEO, MFCC, pitch, etc. Here we are extracting MFCC feature of speech from database. Ms. Anjum Shaikh | Ms. Firdos Shaikh | Mr. Suhaib Ramzan | Prof. M. M. Patil"Clinical Depression Detection Using Speech Feature With Machine Learning Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14363.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/14363/clinical-depression-detection-using-speech-feature-with-machine-learning-approach/ms-anjum-shaikh