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.
