Cross-corpus depression prediction from speech

Abstract

Research on detecting depression from speech has advanced in recent years, but most work has focused on the analysis of one corpus at a time. Given that clinical corpora are typically small, it is important to explore approaches that generalize across corpora and that could ultimately be adapted to new data. We study a new corpus of patient-clinician interactions recorded when patients are admitted to a hospital for suicide risk and again when they are released. To train prediction models, we use the 2014 AVEC challenge German speech dataset, which differs from our data in many factors (including language, context, speakers, and recording conditions). Results reveal that some of the AVEC-trained models predict scores for the clinical data that correlate with both HAM-D depression scores and with the pre-/post-admission ordering. A KL-divergence analysis within the clinical data confirms that the same feature set captures changes correlated with the HAM-D scores. Finally, read versus spontaneous speech samples in both corpora behave differently with respect to the best features and modeling approaches. Implications for the cross-corpus prediction of depression are discussed.