  This study investigates the utility of speech signals for AI-based depression
screening across varied interaction scenarios, including psychiatric
interviews, chatbot conversations, and text readings. Participants include
depressed patients recruited from the outpatient clinics of Peking University
Sixth Hospital and control group members from the community, all diagnosed by
psychiatrists following standardized diagnostic protocols. We extracted
acoustic and deep speech features from each participant's segmented recordings.
Classifications were made using neural networks or SVMs, with aggregated clip
outcomes determining final assessments. Our analysis across interaction
scenarios, speech processing techniques, and feature types confirms speech as a
crucial marker for depression screening. Specifically, human-computer
interaction matches clinical interview efficacy, surpassing reading tasks.
Segment duration and quantity significantly affect model performance, with deep
speech features substantially outperforming traditional acoustic features.
