An interpretable speech foundation model for depression detection by revealing prediction-relevant acoustic features from long speech

Longer speech samples improve accuracy: The model using full-length speech outperformed one using short segments, suggesting that depression detection tools need sufficient audio data to make reliable predictions rather than relying on brief recordings.

The model identifies clinically recognized patterns: The system detected reduced loudness and lower fundamental frequency (F0) as key depression indicators, which aligns with established clinical knowledge about how depression affects speech—making the tool's reasoning transparent to healthcare providers.

Interpretability is essential for clinical use: By revealing which acoustic features drive the model's predictions, this approach makes the AI tool more trustworthy for clinicians to use in practice, rather than presenting it as a "black box" that produces unexplainable results.

Training data quality matters significantly: The research found that segment-level labeling noise (errors in how short audio clips were labeled) was a major problem, highlighting that responsible AI development requires careful attention to data quality, not just model sophistication.

Potential for early screening support: Speech-based depression detection could help with early identification of depression, though the emphasis on interpretability suggests this tool is designed to assist clinicians rather than replace clinical judgment.