Content based clinical depression detection in adolescents

This paper studies the effectiveness of speech contents for detecting clinical depression in adolescents. We also evaluated the performances of acoustic features such as Mel frequency cepstral coefficients (MFCC), short time energy (Energy), zero crossing rate (ZCR) and Teager energy operator (TEO) using Gaussian mixture models for depression detection. A clinical data set of speech from 139 adolescents, including 68 (49 girls and 19 boys) diagnosed as clinically depressed, was used in the classification experiments. Each subject participated in three 20 minutes interactions. The classification was first performed using the whole data and a smaller sub-set of data selected based on behavioural constructs defined by trained human observers (data with constructs). In the experiments, we found that the MFCC+Energy feature out performed the TEO feature. The results indicated that using the construct based speech contents in the problem solving interactions (PSI) session improved the detection accuracy. Accuracy was further improved by 4% when the gender dependent depression modelling technique was adopted. By using construct based PSI session speech content, gender based depression models achieved 65.1% average detection accuracy. Also, for both types of features (TEO and MFCC), the correct classification rates were higher for female speakers than for male speakers.