Empirical Investigation for Predicting Depression from Different Machine Learning Based Voice Recognition Techniques

Over the past few decades, the rate of diagnosing depression and mental illness among youths in both genders has been emerging as a challenging issue in the present society. Adequate numbers of cases that have been prevailing had unheard of symptoms linked to mental depression that are able to be detected using their voice recordings and their messages in social media websites. Due to the wide spread usage of mobile phones, services and social sites emotion prediction and analyzing have been an indispensable part of providing vital care for the eminence of youth’s life. In addition to dynamicity and popularity of mobile applications and services, it is really a challenge to provide an emotion prediction system that can collect, analyze, and process emotional communications in real time and as well as in a highly accurate manner with minimal computation time. Few depression prediction researchers have analyzed and examined that various social networking sites and its activities may be merged to low self-confidence, particularly in young people and adolescents. Moreover, the researchers suggest that several objective voice acoustic measures affected by depression can be detected reliably over the smart phones. And also in some observational study, it is stated that speech samples of patients from the telephone were obtained each week using an IVR system, and voice recording files from smart phones have been under process for predicting the depression. Such that several telephonic standards for obtaining voice data were identified as a crucial factor influencing the reliability and eminence of speech data. Hence, this article investigates on different process applied in different machine learning algorithms in recognizing voice signals which in turn will be used for scrutinizing the techniques for detecting depression levels in future. This will make a blooming change in the youth’s life and solve the social unethical issues in hand.