Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning

Abstract

Digital screening and monitoring applications can aid providers in the management of behavioral health conditions. We explore deep language models for detecting depression, anxiety, and their comorbidity using input from conversational speech. Speech data comprise 16k spoken interactions labeled for both depression and anxiety. We find that results for binary classification range from 0.86 to 0.79 AUC, depending on condition and comorbidity. Best performance occurs for comorbid cases. We show that this result is not attributable to data skew. Finally, we find evidence suggesting that underlying word sequence cues may be more salient for depression than for anxiety.