Analysis of prosodic variation in speech for clinical depression

Understanding how someone is speaking can be equally important to what they are saying when evaluating emotional disorders, such as depression. In this study, we use the acoustic speech signal to analyze variations in prosodic feature statistics for subjects suffering from a depressive disorder. A new sample database of subjects with and without a depressive disorder is collected and pitch, energy, and speaking rate feature statistics are generated at a sentence level and grouped into a series of observations (subset of sentences) for analysis. A common technique in quantifying an observation had been to simply use the average of the feature statistic for the subset of sentences within an observation. However, we investigate the merit of a series of statistical measures as a means of quantifying a subset of feature statistics to capture emotional variations from sentence to sentence within a single observation. Comparisons with the exclusive use of the average show an improvement in overall separation accuracy for other quantifying statistics.