Emotion and Depression Detection from Speech

Abstract

Human speech communication conveys semantic information of the underlying emotions corresponding to the speech of the interlocutor. So, detection of emotions by analysis of speech is important for identifying a subject’s emotional state. Numerous features from human speech are used by convolutional neural network (CNN) and support vector machine (SVM) techniques to detect the emotions such as anger, happiness, fear, sadness, surprise and neutral that are associated with the speech. Prolonged sadness is considered the prerequisite for depression. Monitoring subject’s speech over a period of time helps in detecting clinical depression. Databases of different accents of English language are taken to make sure the system incorporates multiple accents. Emotion and depression detection have applications in fields like lie detection, military, counseling, database access systems, etc.