Abstract: Depression is a mood disorder that causes a persistent feeling of sadness and loss of interest . so called major depressive disorder or clinical depression, it affects how you feel think and behave and can lead to a variety of emotional and physical problems. You may have trouble doing normal day-to-day activities, and sometimes you may feel as if life isn’t worth living.
Early detection and treatment of depression are essential in promoting remission, preventing relapse, and reducing the emotional burden of the disease. Current diagnoses are primarily subjective, inconsistent across professionals, and expensive for the individual who may be in dire need of help. Additionally, early signs of depression are difficult to detect and quantify. These early signs have a promising potential to be quantified by machine learning algorithms that could be implemented in a wearable artificial intelligence (AI) or home device.
This effort addresses an automated device for detecting depression from acoustic features in speech. The tool is aimed at lowering the barrier of entry in seeking help for potential mental illness and supporting medical professionals' diagnoses.
Another method of implementation is through social media data. The social media platform to be used is Twitter. Live tweets are analysed and the model is trained. The project aims to have a dual mode of working between detection through audio and social media data.
Keywords: Machine Learning, Deep Learning, Convolutional Neural Networks, Feature Extraction, Depression Detection, Spectrogram Conversion.
|
DOI:
10.17148/IJARCCE.2022.11684
