I-Vector based depression level estimation technique

Abstract

Depression is considered as a psychosomatic state associated with the soft biometric features. People suffering from depression always behave abnormal. Depression is a clinically proven disorder that can overwhelm a person and his ability to perform even a simple task. Soft biometric provides important information about a person without being enough for their verification because they lack uniqueness. This statement comprises of features which are associated with the psychosomatic state of a person such as feelings, sentiments or brain related disorders like depression. In this paper we have estimated the depression level of each speech signal using I-Vector technique. In our proposed approach first of all we have removed silence from the speech signal then we have extracted features from audio using I-Vector after that split overlapping function is applied to evaluate overlapped audio beats. In the end we have evaluated depression using relationship matrix. We have estimated the depression level of each speaker. This technique has better performance as compared with existing techniques. The overall result has shown that the I-Vector technique has good accuracy to detect depression in audios.