Detection of Adolescent Depression from Speech Using Optimised Spectral Roll-Off Parameters

The purpose of this paper is to examine adolescent depression detection from a clinical database of 63 adolescents (29 depressed and 34 non-depressed) interacting with a parent. A range of spectral roll-off parameters was investigated to observe an association of the frequency energy relationship in relation to depression. The spectral roll-off range improved depression classification rates compared to the best individual roll-off parameter. Further improvement was accomplished using a 2-stage mRMR/SVM feature selection approach to optimize a roll-off parameters subset. The proposed optimized feature set reached an average depression detection accuracy of 82.2% for males and 70.5% for females. More acoustic spectral features were investigated including flux, centroid, entropy, formants and power spectral density to classify depression. The optimized spectral roll-off set was the most effective of the acoustic spectral features. All spectral features, including the best individual spectral roll-off, was grouped into a baseline feature category (S*) with an average classification accuracy of 71.4% (male) and 70.6% (female). A new spectral category (S), with the inclusion of the proposed optimized spectral roll-off sub-set, performed best with an average accuracy of 97.5% (males) and 92.3% (females).