Bilingual Audio Depression Identification Model by Machine Learning

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dc.contributor.authorPoomrittigul, Suvit
dc.contributor.authorKiatrungrit, Komsan
dc.contributor.authorHomsiang, Phanomkorn
dc.contributor.authorTreebupachatsakul, Treesukon
dc.date.accessioned2026-08-06T10:49:12Z
dc.date.available2026-08-06T10:49:12Z
dc.date.issued2025-01-01
dc.description.abstractThe number of depression patients worldwide, particularly in Thailand, is increasing on an upward trend. Depression screening commonly relies on self-report questionnaires. However, these instruments provide subjective assessments. Recent advancements in machine learning technology offer potential improvements in diagnostic accuracy through more objective measures. This study aims to evaluate the effectiveness of machine learning models in classifying depression using a bilingual audio dataset comprising Thai and English languages. Such models have the potential to assist clinicians by providing objective preliminary screening for depression based on vocal analysis, enhancing diagnostic precision and clinical decision-making. Various machine learning models were implemented including KNN, MLP, Random Forest, Decision Tree, SGD, Logistic Regression, SVM, AdaBoost, and Gaussian Naïve Bayes using MFCC-converted audio datasets. The results indicate that machine learning models effectively classify and identify depression even in bilingual audio datasets compared to individual language models, with the highest accuracy reaching 0.95 from MLP and KNN when testing the trained model by a single Thai audio.
dc.identifier.citation2025 International Technical Conference on Circuits Systems Computers and Communications Itc Cscc 2025, 2025
dc.identifier.doi10.1109/ITC-CSCC66376.2025.11137688
dc.identifier.other2-s2.0-105016359557
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16448
dc.source2025 International Technical Conference on Circuits Systems Computers and Communications Itc Cscc 2025
dc.subjectAudio Depression Identification
dc.subjectBilingual Model
dc.subjectEnglish Audio
dc.subjectMachine Learning
dc.subjectThai Audio
dc.titleBilingual Audio Depression Identification Model by Machine Learning
dc.typeConference Paper

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