This research introduces DisNet, a novel approach for learning interpretable representations of depression directly from speech. The study focuses on extracting meaningful acoustic features that can effectively identify and characterize depressive states within vocal patterns. By developing this method, DisNet aims to provide a deeper understanding of how speech characteristics correlate with depression. The significance of DisNet lies in its potential to offer more transparent and explainable models for depression detection using speech data. This interpretability is crucial for advancing research and potentially future clinical applications, as it allows for a clearer insight into the specific speech markers associated with depression.
Original publication date: 2026 Mar 9
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