Speech based Depression Analysis using Web Services and Convolutional Neural Networks

Abstract

People can experience depression, a mood disorder with wide-ranging effects. Depression can happen for many causes in daily life, which has an impact on our health. Stress, a lack of a work-life balance, and other factors make it a serious issue in the current era. It is a key factor in the overall global burden of disease, a leading cause of disability, and a suicide cause. Therapy is now the only effective treatment for depression. However, given the high demand for psychological therapy, it is challenging to find therapists for all potential depression situations. Additionally, there is no reliable biomarker to detect depression in an individual. The most reliable method for identifying depression is sound. Previous research has demonstrated that sad people exhibit fewer prosodic qualities of sound than a normal person. The paper employs deep learning neural networks to teach the computer how to distinguish between a healthy person's sound and a depressed person's sound using speech emotion identification to identify depression. Utilizing a convolutional neural network and the Mel Frequency Cepstral Coefficient, the relevant sound waves may be retrieved. The result is a trained model that can be used to make predictions. This model is then made available as a web service for service requesters to use.