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Thaweesak Yingthawornsuk. Deep learning based classification of depression and suicidal risk among normal speakers using delta-spectral cepstral coefficients. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2025.
Deep learning based classification of depression and suicidal risk among normal speakers using delta-spectral cepstral coefficients
Name: Pavat Ruckchopsanti
Organization :
King Mongkut's University of Technology Thonburi. Department of Media Technology
Email :
pavat776@gmail.com
Name: Nattarika Ngearnsajja
Organization :
King Mongkut's University of Technology Thonburi. Department of Media Technology
Email :
Nattarika.earn@mail.kmutt.ac.th
Name: Pawat Isaraporn
Organization :
King Mongkut's University of Technology Thonburi. Department of Media Technology
Email :
pawatisaraporn@gmail.com
Name: Thanchanok Haruenputh
Organization :
King Mongkut's University of Technology Thonburi. Department of Media Technology
Email :
thanchanok.harue@kmutt.ac.th
Name: Thaweesak Yingthawornsuk
Organization :
King Mongkut's University of Technology Thonburi. Department of Media Technology
Email :
thaweesak.yin@kmutt.ac.th
LCSH:
Depression, Mental
-- Diagnosis
LCSH:
Suicide
-- Risk factors
Abstract:
This study utilizes deep learning techniques to
develop a precise model for identifying audio recordings, of
female speakers for their mental health conditions. The female
speakers were categorized into three volunteer groups, which
are remitted (RMT) , depressed ( DPR), and high-risk suicidal
(HRK) by psychiatrist. The various deep learning techniques
such as CNN1D, CNN2D, SVM and LSTM were thoroughly
trained and validated with our feature samples which are the
Delta MFCC (ΔMFCC), representing the vocal-tract frequency
response associated with different categorized emotional
illnesses of three studied volunteer groups. The experimental
results show that the CNN2D model achieved the highest
accuracies of 0.95 and 0.99 among studied models in classifying
ΔMFCC samples between RMT and DPR groups, and between
RMT and HRK groups, respectively. Moreover, precision and
recall scores were also robust for the CNN models. In contrast,
the SVM model achieved with accuracy of 0.83 as compare to
CNN models while its precision and recall found to be
adequately high. The findings can significantly contribute to
understanding of affected vocal characteristics of spoken sound
samples associated with mental illness conditions
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2025
Modified:
2025-06-25
Issued:
2025-06-25
บทความ/Article
application/pdf
BibliograpyCitation :
In Electrical Engineering Academic Association (Thailand) and King Mongkut's University of Technology North Bangkok. Department of Electrical and Computer Engineering. 13th International Electrical Engineering Congress (iEECON 2025) (P06431). Bangkok : Electrical Engineering Academic Association (Thailand), 2025
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
0.020398 วินาที
Pavat Ruckchopsanti
| Title | Contributor | Type |
|---|---|---|
| Deep learning based classification of depression and suicidal risk among normal speakers using delta-spectral cepstral coefficients
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Pavat Ruckchopsanti;Nattarika Ngearnsajja;Pawat Isaraporn;Thanchanok Haruenputh;Thaweesak Yingthawornsuk | บทความ/Article |
Nattarika Ngearnsajja
| Title | Contributor | Type |
|---|---|---|
| Deep learning based classification of depression and suicidal risk among normal speakers using delta-spectral cepstral coefficients
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Pavat Ruckchopsanti;Nattarika Ngearnsajja;Pawat Isaraporn;Thanchanok Haruenputh;Thaweesak Yingthawornsuk | บทความ/Article |
Pawat Isaraporn
| Title | Contributor | Type |
|---|---|---|
| Deep learning based classification of depression and suicidal risk among normal speakers using delta-spectral cepstral coefficients
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Pavat Ruckchopsanti;Nattarika Ngearnsajja;Pawat Isaraporn;Thanchanok Haruenputh;Thaweesak Yingthawornsuk | บทความ/Article |
Thanchanok Haruenputh
| Title | Contributor | Type |
|---|---|---|
| Deep learning based classification of depression and suicidal risk among normal speakers using delta-spectral cepstral coefficients
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Pavat Ruckchopsanti;Nattarika Ngearnsajja;Pawat Isaraporn;Thanchanok Haruenputh;Thaweesak Yingthawornsuk | บทความ/Article |
Thaweesak Yingthawornsuk
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| Deep learning based classification of depression and suicidal risk among normal speakers using delta-spectral cepstral coefficients
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Pavat Ruckchopsanti;Nattarika Ngearnsajja;Pawat Isaraporn;Thanchanok Haruenputh;Thaweesak Yingthawornsuk | บทความ/Article |