KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Homsiang, PhanomkornHas files: No

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Bilingual Audio Depression Identification Model by Machine Learning
    (2025-01-01)
    Poomrittigul, Suvit
    ;
    Kiatrungrit, Komsan
    ;
    Homsiang, Phanomkorn
    ;
    Treebupachatsakul, Treesukon
    The 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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Classification of Depression Audio Data by Deep Learning
    (2022-01-01)
    Homsiang, Phanomkorn
    ;
    Treebupachatsakul, Treesukon
    ;
    Kiatrungrit, Komsan
    ;
    Poomrittigul, Suvit
    Due to many factors such as anxiety from contracting the disease and concern about the socioeconomic impacts, Thai people have accumulated stress and are at risk of depression. The diagnosis of depression can be primarily assessed by testing the assessments such as PHQ8, PHQ-9, and CES-D. The applied deep learning technology in medicine has received research interest and has been developing. In this research, we tried the classification of depression and non-depression audio datasets with the implementation of 4 model architectures: 1D CNN, 2D CNN, LSTM, and GRU. By converting wave audio format (WAV) of Daic-woz database to the Melfrequency cepstrum (MFC). We have done the training and evaluated the 4 model architectures and compared the results between non-augmented and augmented datasets. The highest accuracy was obtained from 1D CNN with a non-data augmentation of 95%, and a 2D CNN with a data augmentation of 75%. These results confirm that human voices can differentiate between depression and non-depression.