Emotion Recognition in Speech of Parents of Depressed Adolescents

This paper investigates automatic affect classification in spontaneous speech within normal and clinical family environments. The data base used in this study comprised speech recordings of parents of depressed adolescents (19 fathers and 20 mothers) and parents of non-depressed adolescents (25 fathers and 7 mothers). The speech data were recorded during natural parent-child conversations. Five emotional classes were considered: neutral, angry, anxious, dysphoric, and happy. Four different combinations of features (set A, B, C, and D) derived from the Teager energy operator (TEO) and two different classifiers: probabilistic neural network (PNN) and Gaussian mixture model (GMM) were tested and compared. The feature extraction process was combined with an optimal feature selection algorithm based on the mutual information criteria. The GMM classifier provided consistently higher correct classification rates (49.6% to 62.0%) compared with the PNN classifier (31.6% to 42.7%). Set C/GMM was found to be the best performing feature/classifier combination. In all cases, the classification rates for parents of depressed adolescents were higher than for parents of non-depressed adolescents. Similarly, the classification rates for mothers were higher than for fathers. The results appear to suggest that parents of depressed adolescents express their emotions with higher degree of discrimination between different types of affect than parents of non-depressed adolescents. Similarly, mothers appear to express their affect with higher degree of discrimination between different types of affect than fathers.