Abstract
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 #frequencyenergy 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.
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