Early Mental Health Uncovering with Short Scripted and Unscripted Voice Recordings

ML Tlachac, Ricardo Flores, Ermal Toto, Elke A. Rundensteiner
Deep Learning Applications (DLAV), vol 4, Springer, 2023

Mental illnesses are often undiagnosed, highlighting the need for an effective alternative to traditional screening surveys. We propose our Early Mental Health Uncovering (EMU) framework that conducts rapid mental illness screening with active and passive modalities. We designed, deployed, and evaluated the EMU app to passively collect retrospective digital phenotype data and actively collect short voice recordings. The EMU app also administered a depression screening survey to label the data. We collected data from crowdsourced and student populations, both of whom shared sufficient voice recordings for modeling. We thus assess the classification ability of machine learning and deep learning models trained with scripted and unscripted voice recordings. For the crowdsourced participants, machine learning models screened for depression with an AUC of 0.78 and suicidal ideation with an AUC of 0.73. For the student participants, deep learning models screened for depression with an AUC of 0.70 and suicidal ideation an AUC of 0.72. Combining datasets did not improve screening capabilities, though the best performing models on the combined dataset notably required the voice transcripts. This research facilitates a better understanding of modality selection for mobile screening. We will make the features publicly available to further advance mental illness screening research.