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

Abstract

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.

The Association between Depression Severity, Prosody, and Voice Acoustic Features in Women with Depression

“…It is because these patients are not talkative; rather, they speak with more hesitation and variable pauses that result in a reduced speaking rate, especially by increasing the severity of the problem [11]. Tis is according to Cepstral peak prominence, 2 fundamental frequency, 3 standard deviation of fundamental frequency, 4 harmonic-to-noise ratio, and 5 seconds.…”

“…Finding new methods to determine a disease or predict its progression is a state-of-the-art issue in recent studies, such as using machine learning for Parkinson's disease [ 1 ], hepatitis [ 2 ], and depression disorder [ 3 , 4 ] or analyzing voice acoustics and prosody to determine depression disorder [ 5 ].…”

“…In a study, a significantly lower CPP was reported in PWDD compared with the healthy group [ 19 ]. Moreover, some findings indicated that other parameters such as cepstral, spectral, Mel-frequency cepstrum coefficients, and loudness could be considered to screen PWDD from healthy people [ 3 , 25 , 26 ]. Unlike the other acoustic evaluation methods, CPP is not dependent on pitch tracking to detect the amount of perturbation of the voice signals.…”

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

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”