Deep Learning-Based Predictive Modeling for Male Depression Detection
This project utilizes machine learning techniques to construct a highly precise model for categorizing audio recordings, with a particular focus on male speakers and their mental health conditions. The audio recordings are classified into three distinct categories: Remitted (RMT), Depressed (D...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
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INTI International University
2025
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2124/ http://eprints.intimal.edu.my/2124/2/665 http://eprints.intimal.edu.my/2124/3/joit2025_02b.pdf |
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| author | R. S. Lakshmi, Balaji Sumetee, Jirapattarasakul Kantapat, Kwansomkid Sirimonpak, Suwannakhun Thaweesak, Yingthawornsuk |
| author_facet | R. S. Lakshmi, Balaji Sumetee, Jirapattarasakul Kantapat, Kwansomkid Sirimonpak, Suwannakhun Thaweesak, Yingthawornsuk |
| author_sort | R. S. Lakshmi, Balaji |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | This project utilizes machine learning techniques to construct a highly precise model for
categorizing audio recordings, with a particular focus on male speakers and their mental health
conditions. The audio recordings are classified into three distinct categories: Remitted (RMT),
Depressed (DPR), and High-risk for suicide (HRK), with special attention to gender-specific
nuances. We have conducted an extensive exploration and comparison of diverse machine learning
models, including 1D and 2D Convolutional Neural Networks (CNNs), Support Vector Machine
(SVM), and Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM). Our
primary goal is to identify the most accurate model for classifying these male audio recordings,
potentially offering a valuable tool for early detection and intervention in male mental health
issues. We eagerly look forward to sharing our research results, aiming to make a substantial
contribution to the understanding and treatment of depression among males. In this paper, we
present the results of our investigation, comparing the accuracy of audio classification using 25-
second and 1-minute speech segmentation |
| first_indexed | 2025-11-14T11:58:54Z |
| format | Article |
| id | intimal-2124 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:54Z |
| publishDate | 2025 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-21242025-06-11T06:39:30Z http://eprints.intimal.edu.my/2124/ Deep Learning-Based Predictive Modeling for Male Depression Detection R. S. Lakshmi, Balaji Sumetee, Jirapattarasakul Kantapat, Kwansomkid Sirimonpak, Suwannakhun Thaweesak, Yingthawornsuk QA75 Electronic computers. Computer science T Technology (General) TK Electrical engineering. Electronics Nuclear engineering This project utilizes machine learning techniques to construct a highly precise model for categorizing audio recordings, with a particular focus on male speakers and their mental health conditions. The audio recordings are classified into three distinct categories: Remitted (RMT), Depressed (DPR), and High-risk for suicide (HRK), with special attention to gender-specific nuances. We have conducted an extensive exploration and comparison of diverse machine learning models, including 1D and 2D Convolutional Neural Networks (CNNs), Support Vector Machine (SVM), and Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM). Our primary goal is to identify the most accurate model for classifying these male audio recordings, potentially offering a valuable tool for early detection and intervention in male mental health issues. We eagerly look forward to sharing our research results, aiming to make a substantial contribution to the understanding and treatment of depression among males. In this paper, we present the results of our investigation, comparing the accuracy of audio classification using 25- second and 1-minute speech segmentation INTI International University 2025-02 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2124/2/665 text en cc_by_4 http://eprints.intimal.edu.my/2124/3/joit2025_02b.pdf R. S. Lakshmi, Balaji and Sumetee, Jirapattarasakul and Kantapat, Kwansomkid and Sirimonpak, Suwannakhun and Thaweesak, Yingthawornsuk (2025) Deep Learning-Based Predictive Modeling for Male Depression Detection. Journal of Innovation and Technology, 2025 (02). pp. 1-12. ISSN 2805-5179 http://ipublishing.intimal.edu.my/joint.html |
| spellingShingle | QA75 Electronic computers. Computer science T Technology (General) TK Electrical engineering. Electronics Nuclear engineering R. S. Lakshmi, Balaji Sumetee, Jirapattarasakul Kantapat, Kwansomkid Sirimonpak, Suwannakhun Thaweesak, Yingthawornsuk Deep Learning-Based Predictive Modeling for Male Depression Detection |
| title | Deep Learning-Based Predictive Modeling for Male Depression Detection |
| title_full | Deep Learning-Based Predictive Modeling for Male Depression Detection |
| title_fullStr | Deep Learning-Based Predictive Modeling for Male Depression Detection |
| title_full_unstemmed | Deep Learning-Based Predictive Modeling for Male Depression Detection |
| title_short | Deep Learning-Based Predictive Modeling for Male Depression Detection |
| title_sort | deep learning-based predictive modeling for male depression detection |
| topic | QA75 Electronic computers. Computer science T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.intimal.edu.my/2124/ http://eprints.intimal.edu.my/2124/ http://eprints.intimal.edu.my/2124/2/665 http://eprints.intimal.edu.my/2124/3/joit2025_02b.pdf |