Algorithm Classifying Depressed Patients’Speech Samples Using Deep Learning

This research uses deep learning techniques
to create a highly accurate model for identifying audio
recordings, with an emphasis on female speakers and
their mental health difficulties. Based on gender, the audio
recordings are categorized into three categories: Remitted,
Depressed and High-risk for suicide. We studied and compared
different deep learning algorithms in depth. Neural networks
include 1D and 2D Convolutional Neural Networks (CNNs),
Support Vector Machines (SVMs), and Recurrent Neural
Networks with Long Short-Term Memory (RNN-LSTM).
Specifically, between Remitted vs. Depressed and
Remitted vs. High risk for suicide, utilizing 45-second
and 2-minute speech segmented data, respectively.
In this category, the CNN1D model achieved an accuracy of 0.92
and 0.98, accompanied by robust precision-recall scores
of 0.92-0.98 and 0.92-0.98, respectively. The CNN2D model
performed even better with an accuracy of 0.95 and 0.99, along
with equally commendable robust precision-recall scores of
0.95-0.99 and 0.95-0.99, respectively. However, the LSTM model
struggled in this category, managing only an accuracy of 0.98
and 0.95 with a precision of 0.89 and 0.85 and a recall
of 0.89 and 0.85. Conversely, the SVM model excelled with an
accuracy of 0.83 and 0.98, boasting a precision of 0.93 and 0.95
and a recall of 0.93 and 0.97. All precision and recall accuracy
of the model were calculated from each number of FN and FP.
We are excited to publish our findings and hope to make
a substantial contribution to the knowledge and treatment
of female depression. We are glad to provide the outcomes
of our study.