This paper investigates how well eGeMAPS features can be used to classify depression from a patient’s speech audio samples through the use of statistical resampling methods. We use permutation tests to evaluate, with high confidence, whether eGeMAPS features and the speaker’s depression status are dependent. We use bootstrap confidence intervals to test, with high confidence, whether eGeMAPS features are able to better discriminate depression in male speakers than in female speakers. Lastly, we compare the detection power of different subsets of the eGeMAPS features. We use an open-source dataset of depressed and non-depressed speakers (E-DAIC), an open-source audio feature extractor (eGeMAPS), and open-source machine learning classifiers (WEKA) to enable replication of results and establish a baseline for future studies.
Turnipseed et al. (Wed,) studied this question.