  While speech-based depression detection methods that use speaker-identity
features, such as speaker embeddings, are popular, they often compromise
patient privacy. To address this issue, we propose a speaker disentanglement
method that utilizes a non-uniform mechanism of adversarial SID loss
maximization. This is achieved by varying the adversarial weight between
different layers of a model during training. We find that a greater adversarial
weight for the initial layers leads to performance improvement. Our approach
using the ECAPA-TDNN model achieves an F1-score of 0.7349 (a 3.7% improvement
over audio-only SOTA) on the DAIC-WoZ dataset, while simultaneously reducing
the speaker-identification accuracy by 50%. Our findings suggest that
identifying depression through speech signals can be accomplished without
placing undue reliance on a speaker's identity, paving the way for
privacy-preserving approaches of depression detection.
