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This work shows that depression changes the correlation between featuresextracted from speech. Furthermore, it shows that using such an insight canimprove the training speed and performance of depression detectors based onSVMs and LSTMs. The experiments were performed over the Androids Corpus, apublicly available dataset involving 112 speakers, including 58 peoplediagnosed with depression by professional psychiatrists. The results show thatthe models used in the experiments improve in terms of training speed andperformance when fed with feature correlation matrices rather than with featurevectors. The relative reduction of the error rate ranges between 23.1% and26.6% depending on the model. The probable explanation is that featurecorrelation matrices appear to be more variable in the case of depressedspeakers. Correspondingly, such a phenomenon can be thought of as a depressionmarker.

The Relationship Between Speech Features Changes When You Get Depressed:  Feature Correlations for Improving Speed and Performance of Depression  Detection