International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 6 Issue 6
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Identification of Depression from the Speech of a Person

Author(s) Rutik Deshmukh, Supriya Lande, Onkar Bhosale, Akshay Waghmode, Anupama Phakatkar
Country India
Abstract The world’s population has experienced profound psychological effects over time due to stress, anxiety, and fast-paced modern lifestyles. Accurate mapping of the diverse forms of human biology is made possible by the global
technological development in healthcare, which digitizes scopious data more than usual measuring methods. Healthcare data analysis can now be done more effectively with the use of machine learning (ML), thanks to its vast volume of data. To forecast the likelihood of mental illnesses and, consequently, carry out possible treatment outcomes, machine learning techniques are being applied to the field of mental health. This review paper outlines many machine learning techniques that are employed in the identification of depression. Classification, Deep learning, and ensemble—are used to group the machine learning-based depression detection systems. A comprehensive approach for identification depression is introduced, which includes data extraction, pre-processing, ML classifier training, detection, classification, and performance assessment. Additionally, it provides a summary that identifies the goals and constraints of various research projects that have been presented in the field of depression detection. It also covers potential directions for future research in the realm of depression diagnosis
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-12
Cite This Identification of Depression from the Speech of a Person - Rutik Deshmukh, Supriya Lande, Onkar Bhosale, Akshay Waghmode, Anupama Phakatkar - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.16443
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.16443
Short DOI https://doi.org/gtqx2h
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