Depression is a psychiatric mood disorder that significantly affects an individual's emotional state and functional abilities. It is often associated with other medical conditions, and this is particularly relevant in the context of Parkinson's disease, where non-motor symptoms, including depression, pose substantial challenges to the well-being of the individuals. Detecting depression through speech analysis has gained prominence due to the perceptible alterations in speech patterns influenced by emotional and cognitive changes. However, the accurate extraction and interpretation of these acoustic features remains challenging, especially in speech difficulties such as those with Parkinson's disease. This thesis seeks to address this challenge by employing various machine learning techniques to identify depression through speech analysis and to make the most indicative acoustic features for depression interpretable from speech signals of healthy individuals and patients affected by Parkinson's.