Clinical Depression Detection Using Speech Feature With Machine Learning Approach

Abstract

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

Authors and Affiliations

Keywords

Related Articles

Social Security for Women Workers in Unorganized Sector A Study

"Social security means the overall security for a person in the family, work place and society. Social security is a system to meet the basic needs as well as contingencies of life in order to maintain an adequate standa...

Aluminium Alloy Scraps as Useful Raw Material in Component Manufacturing and the Attendant ILL Effects

The roles and responsibilities of Human Resources departments are transforming as the modern business faces pressures of globalization. The global supply of talent is short of its long-term demand, and the gap is a chall...

Li Fi the Technology beyond Wi Fi

Today pace of net is an essential multifaceted nature and all individuals be it undertaking, foundations, enterprises, business visionaries is main thrust for buying accurate data at the specific time and definite spot....

Mother's work profile in tribal communities and its effect on child feeding

Background: Female labour participation is considered strong indicator of the growth of a country. Women in the rural set up constitute 26.7% of the workforce in 2015 “ 2016 (ILO, 2016). This study was taken up to unders...

Study on Buildup the Properties of R.C.C. Structures against Fire

Fire remains one of the serious potential risks to most buildings and structures. Since concrete is widely used in construction, research on fire resistance of concrete becomes more and more important. Many researchers a...

Download PDF file
  • EP ID EP361807
  • DOI -
  • Views 101
  • Downloads 0

How To Cite

(2018). Clinical Depression Detection Using Speech Feature With Machine Learning Approach. International Journal of Trend in Scientific Research and Development, 2(4), -. https://europub.co.uk/articles/-A-361807