Comparing objective feature statistics of speech for classifying clinical depression

Abstract

Human communication is saturated with emotional context that aids in interpreting a speakers mental state. Speech analysis research involving the classification of emotional states has been studied primarily with prosodic (e.g., pitch, energy, speaking rate) and/or spectral (e.g., formants) features. Glottal waveform features, while receiving less attention (due primarily to the difficulty of feature extraction), have also shown strong clustering potential of various emotional and stress states. This study provides a comparison of the major categories of speech analysis in the application of identifying and clustering feature statistics from a control group and a patient group suffering from a clinical diagnosis of depression.