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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: March 2026
Volume 11 | Issue 3
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Impact Factor : 8.14
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| Paper Title: | Depression Detection based on Speech format with Deep learning and Advanced Voice processing Techniques |
| Authors Name: | Ch.Lavanya Susanna , S.Srilakshmi Ramya , K. Chamundeswari , A. Tulasi , M. Meghana |
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IJRTI_189319
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| Published Paper Id: | IJRTI2403041 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | Depression is the most common type of physiological or mental disorder, affecting a significant portion of the global population. sadness sufferers deal with a wide range of problems, including anxiety, loneliness, and sadness. Facial expression recognition is one creative method of human-computer interaction that has emerged recently. It has several uses, such as virtual reality gaming, online education, driving, security, and more. Facial expressions are one of the main ways that people express their innermost thoughts and feelings. A person's facial expressions can sometimes give away a lot about his innermost feelings. Deep learning algorithms, such as convolutional neural networks and radiofrequency (RF) technology, are used in the construction of the system. Finally, the hopelessness is evident. Speech can therefore be used to diagnose depression. One of the most common mental illnesses affecting a large number of people worldwide is depression. Many other symptoms, including anxiety, loneliness, and profound grief, are often present as well. Recent technological advancements have enabled novel approaches to the diagnosis and treatment of depression, with speech analysis serving as the primary diagnostic tool. The prospective applications of facial expression detection in virtual reality games and security systems, among other industries, have generated interest. However, the study of speech patterns and content has gained increasing importance in the diagnosis of depression. This shift in focus recognizes the role language plays in communicating emotional and psychological well-being. Deep learning algorithms, particularly convolutional neural networks, and sophisticated audio processing techniques have made it possible to identify sensitive indications of depression in spoken language. |
| Keywords: | Recognizing depression, identifying facial expressions, analysing speech Convolutional neural networks (CNN), radiofrequency (RF) neural networks, accurate diagnosis |
| Cite Article: | "Depression Detection based on Speech format with Deep learning and Advanced Voice processing Techniques", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.9, Issue 3, page no.278 - 286, March-2024, Available :http://www.ijrti.org/papers/IJRTI2403041.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2403041
Registration ID:189319
Published In: Volume 9 Issue 3, March-2024
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Page No: 278 - 286 Country: vijayawada-8,krishna dist, Andhra Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2403041 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2403041 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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