Vocal modulation features in the prediction of major depressive disorder severity

Abstract

This thesis develops a model of vocal modulations up to 50 Hz in sustained vowels as a basis for biomarkers of neurological disease, particularly Major Depressive Disorder (MDD). Two model components contribute to amplitude modulation (AM): AM from respiratory muscles and from interaction between formants and frequency modulation in the fundamental frequency harmonics. Based on the modulation model, we test three methods to extract the envelope of the third formant from which features are extracted using sustained vowels from the 2013 AudioNisual Emotion Challenge. Using a Gaussian-Mixture-Model-based predictor, we evaluate performance of each feature in predicting subjects' Beck MDD severity score by the root mean square error (RMSE), mean absolute error (MAE), and Spearman correlation between the actual Beck score and predicted score. Our lowest MAE and RMSE values are 8.46 and 10.32, respectively (Spearman correlation=0.487, p<0.001), relative to the mean MAE of 10.05 and mean RMSE of 11.86.