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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 de...

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Autores principales: Punithavathi, R., Sharmila, M., Avudaiappan, T., Raj, I. Infant, Kanchana, S., Mamo, Samson Alemayehu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010190/
https://www.ncbi.nlm.nih.gov/pubmed/35432567
http://dx.doi.org/10.1155/2022/6395860
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author Punithavathi, R.
Sharmila, M.
Avudaiappan, T.
Raj, I. Infant
Kanchana, S.
Mamo, Samson Alemayehu
author_facet Punithavathi, R.
Sharmila, M.
Avudaiappan, T.
Raj, I. Infant
Kanchana, S.
Mamo, Samson Alemayehu
author_sort Punithavathi, R.
collection PubMed
description 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.
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spelling pubmed-90101902022-04-15 Empirical Investigation for Predicting Depression from Different Machine Learning Based Voice Recognition Techniques Punithavathi, R. Sharmila, M. Avudaiappan, T. Raj, I. Infant Kanchana, S. Mamo, Samson Alemayehu Evid Based Complement Alternat Med Research Article 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. Hindawi 2022-04-07 /pmc/articles/PMC9010190/ /pubmed/35432567 http://dx.doi.org/10.1155/2022/6395860 Text en Copyright © 2022 R. Punithavathi et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Punithavathi, R.
Sharmila, M.
Avudaiappan, T.
Raj, I. Infant
Kanchana, S.
Mamo, Samson Alemayehu
Empirical Investigation for Predicting Depression from Different Machine Learning Based Voice Recognition Techniques
title Empirical Investigation for Predicting Depression from Different Machine Learning Based Voice Recognition Techniques
title_full Empirical Investigation for Predicting Depression from Different Machine Learning Based Voice Recognition Techniques
title_fullStr Empirical Investigation for Predicting Depression from Different Machine Learning Based Voice Recognition Techniques
title_full_unstemmed Empirical Investigation for Predicting Depression from Different Machine Learning Based Voice Recognition Techniques
title_short Empirical Investigation for Predicting Depression from Different Machine Learning Based Voice Recognition Techniques
title_sort empirical investigation for predicting depression from different machine learning based voice recognition techniques
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010190/
https://www.ncbi.nlm.nih.gov/pubmed/35432567
http://dx.doi.org/10.1155/2022/6395860
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