Depression is a common mental disorder that affects more than 264 million people worldwide. Between 76% and 85% of people in low and middle-income countries receive no treatment for their disorder(P. S. Wang et al.,2017). There are many barriers to effective treatment such as social stigma, lack of resources, and shortage of trained professionals employed in mental health facilities to mention but a few. This study aims to investigate how machine learning algorithms can be used to create self-help applications that detect depression from vocal acoustic features and suggest self-help remedies to bridge the treatment gap.