Characterizing sub-band spectral entropy based acoustics as assessment of vocal correlate of depression

The empirical results of investigating vocal correlate of depression in female adults are presented in that the certain acoustical property of spoken sound based on spectral entropy is capable of relating the affect change in speech with the symptom severity in diagnostic speakers. Studied sub-band entropies achieved the 93% correct classification in classifying two classes of depressed and remitted speech samples with Support Vector Machine (SVM). By validating the entropy feature models modified on the basis of F-ratio measures, the improvement in classification performances is significantly increased.