Depression Detection from Speech Emotion Recognition

Abstract

The recognition of the internal emotional state of a person plays an important role in several humanrelated fields. Emotions constitute an essential part of our existence as it exerts great influence on the physical and mental health of people. Depression is a common mental disorder. Developments in affective sensing technology with focus on acoustic features will potentially bring a change due to depressed patients’ slow, hesitating, monotonous voice as remarkable characteristics. This paper will present classification of emotions and from it, depression is detected by using speech signals. Both time and frequency domain features will be used in feature vector extraction. In feature extraction, the paper will use wavelet transform and MFCC. DenseNet will be used to detect the emotion, classify the type of emotion and then depression.