Automatic Depression Detection Using Smartphone-Based Text-Dependent Speech Signals: Deep Convolutional Neural Network Approach.
Ah Young KimEun Hye JangSeung Hwan LeeKwang-Yeon ChoiJeon Gue ParkHyun-Chool ShinPublished in: Journal of medical Internet research (2023)
Checking the mood of patients with major depressive disorder and detecting the consistency of objective descriptions are very important research topics. This study suggests that the analysis of speech data recorded while reading text-dependent sentences could help predict depression status automatically by capturing the characteristics of depression. Our method is smartphone based, is easily accessible, and can contribute to the automatic identification of depressive states.
Keyphrases
- major depressive disorder
- bipolar disorder
- deep learning
- convolutional neural network
- sleep quality
- depressive symptoms
- machine learning
- smoking cessation
- electronic health record
- physical activity
- working memory
- hearing loss
- stress induced
- quantum dots
- loop mediated isothermal amplification
- neural network
- sensitive detection