Mel frequency cepstral feature and Gaussian Mixtures for modeling clinical depression in adolescents

Abstract

With suicidal behavior being linked to depression that starts at an early age of a person's life, many investigators are trying to find early tell-tale signs to assist psychologists in detecting clinical depression through acoustic analysis of a patient's speech. The purpose of this paper was to study the effectiveness of Mel frequency cepstral coefficients (MFCCs) in capturing the overall mental state of a patient through the analysis of their various vocal emotions displayed during 20 minutes of problem-solving interaction sessions. We also propose both gender based and gender independent clinical depression models using Gaussian mixture models. Experiments on 139 adolescents subject corpus indicates that incorporation of both first and second time derivatives of MFCCs can improve the overall classification accuracy by 3%. Gender differences proved to be a factor in improving clinical depressed subject detection, where gender based models outperformed the gender independent models by 8%.