Source-linked AI summary
Towards Interpretable Depression Detection: Linking Acoustic Features to DSM-5 Indicators
Jonas Länzlinger, Katharina O. E. Müller, Burkhard Stiller, Bruno Rodrigues
TL;DR
Depression detection needs approaches that address subjective assessment, interpretability, and privacy limitations. This paper introduces a transparent Linkage Framework mapping speech acoustics to DSM-5 indicators locally, with preliminary DAIC-WOZ results showing directionally consistent associations and edge-feasible processing. The findings support the framework’s design rationale while motivating broader validation.
Problem
Depression assessment relies on subjective self-reports, while speech-based systems often lack interpretable symptom-level outputs and cloud processing creates privacy risks.
Method
The paper uses a transparent Linkage Framework that explicitly maps acoustic features to DSM-5 indicators within a locally running speech-processing pipeline.
Results
Preliminary DAIC-WOZ evaluation finds directionally consistent associations for reduced pitch variability, longer pauses, and slower speech tempo, with real-time edge throughput below 1 s per 10 s window.
Takeaways & Limitations
The framework supports interpretable, symptom-level review while keeping raw speech at home and provides a design basis for transparent mental health monitoring.
Takeaways & Limitations
DAIC-WOZ uses single-session interview speech, the sample is n=64, and the current framework covers four of nine DSM-5 indicators.
Abstract
from arXiv · showhide
Depression affects millions worldwide, yet diagnosis relies on subjective self-reports that may miss authentic behavior. This paper presents an approach linking speech acoustics to DSM-5 depressive-behavior indicators through a transparent Linkage Framework. Unlike black-box models, the framework explicitly maps acoustic features (pitch variability, pauses, speech tempo) to clinical indicators, enabling interpretable, indicator-level outputs. The system runs locally on commodity hardware (HW) to preserve privacy. Preliminary evaluation on DAIC-WOZ shows directionally consistent associations between acoustic features and DSM-5 indicators for psychomotor change and concentration difficulty, supporting the design rationale. Future work will validate on longitudinal datasets and extend multimodal integration while maintaining edge constraints.
I. INTRODUCTION
The paper targets interpretability and privacy barriers in depression detection by linking speech acoustics to DSM-5 indicators through a transparent, local framework. Preliminary DAIC-WOZ evaluation reports directionally consistent feature–indicator associations.
- Clinical diagnosis can suffer from recall errors and subjectivity, while existing systems face interpretability and privacy barriers.
- Deep models often output aggregate probabilities without showing which symptoms are elevated or why, limiting symptom-level clinical reasoning.
- Local processing keeps raw audio from leaving the home, addressing privacy risks associated with sensitive household speech data.
- Speech is selected as an acoustic modality because it carries clinically validated depression cues and can be processed transparently and efficiently at the edge.
- The proposed Linkage Framework maps acoustic features to DSM-5 depressive-behavior indicators through explicit, testable rules.
- Preliminary DAIC-WOZ evaluation shows directionally consistent feature–indicator associations, while the approach targets efficient edge execution.
II. SYSTEM DESIGN
The system uses a structured on-device pipeline that transforms captured speech into interpretable DSM-5 indicator scores. Its architecture is organized from audio ingestion through feature extraction, temporal context, and analysis.
- Figure 1 presents the high-level data pipeline from audio ingestion to DSM-5 indicator scoring.
- The architecture moves from audio ingestion and voice-activity detection to descriptor extraction, temporal smoothing, and DSM-5 indicator mapping.
- All processing after ingestion runs on-device to preserve privacy.
A. Linkage Framework (LF)
The Linkage Framework makes the path from acoustic measurements to DSM-5 indicators explicit, modular, and clinically grounded. Its many-to-many mappings combine multiple acoustic cues to support symptom interpretation.
- A. Linkage Framework (LF): The Linkage Framework explicitly and testably bridges raw audio features with DSM-5 symptom indicators.
- A. Linkage Framework (LF): Five layers transform measurements into features, biomarkers, indicators, and an analysis counting rule.
- A. Linkage Framework (LF): Prosodic features capture pitch, loudness, and timing, while source and quality features reflect phonatory stability and breathiness.
- A. Linkage Framework (LF): The framework focuses on DSM-5 indicators most observable from speech rather than all nine indicators.
- A. Linkage Framework (LF): Clinical links associate reduced F0 variability, longer pauses, slower articulation, jitter, and shimmer with depressive speech characteristics.
- A. Linkage Framework (LF): Many-to-many mappings let one feature support multiple indicators and each indicator draw on multiple features, improving robustness to noise and missing data.
B. Computing Indicator Scores
Indicator scores are computed through standardized, directionally aligned features followed by temporal smoothing and a DSM-5 decision rule. Thresholds can be personalized using periodic PHQ-9 self-reports.
- Features are z-score standardized with outliers clipped at ±3σ before directional transforms align deviations with indicator semantics.
- An exponential moving average provides temporal stability, with βi controlling each indicator’s response.
- Indicator presence is determined when the smoothed score reaches its threshold.
- The DSM-5 decision rule requires at least five indicators, including depressed mood or loss of interest.
III. PRELIMINARY EVALUATION
The preliminary evaluation tests whether the Linkage Framework yields directionally consistent feature–indicator associations and supports real-time edge performance.
- The evaluation assesses directional consistency between acoustic features and DSM-5 indicators.
- The evaluation also assesses whether the pipeline achieves real-time performance on edge devices.
A. Dataset and Hypotheses
The study uses DAIC-WOZ speech data and tests four hypotheses linking acoustic patterns to psychomotor change and concentration difficulty.
- Dataset: DAIC-WOZ provides speech recordings with PHQ-8 responses aligned with DSM-5 indicators.The balanced sample includes 64 participants, evenly split by sex, processed in 10 s windows.
- Hypotheses: The hypotheses target psychomotor change and concentration difficulty using pitch variability, pauses, energy dynamics, and speech tempo.The tested directions are reduced pitch variability, longer pauses, lower energy dynamics, and slower speech tempo.
- Hypotheses: Table II reports the preliminary hypothesis outcomes on DAIC-WOZ.
- Hypotheses: Figure 3 summarizes effect sizes for feature–indicator pairs, with darker shading denoting stronger effects.
B. Results
Reduced pitch variability, longer pauses, and slower speech tempo show directionally consistent associations with the hypothesized indicators, while energy dynamics are weaker.
- Reduced pitch variability, longer pauses, and slower speech tempo show directionally consistent associations with the hypothesized indicators.
- Energy dynamics show weaker effects than the other tested acoustic patterns.
- Effect sizes remain moderate and do not reach significance after FDR correction.The passage attributes this result to depression’s multifactorial nature and DAIC-WOZ’s limited sample size.
- The directional consistency supports the Linkage Framework design.
C. Temporal Behavior
Streaming evaluation shows that the Linkage Framework accumulates sustained patterns while damping transient spikes through EMA smoothing.
- The Linkage Framework correctly accumulates sustained patterns and dampens transient spikes via EMA smoothing.
- Brief emotional fluctuations do not trigger false positives in the streaming evaluation.
- Indicator scores rise only after consistent patterns persist across multiple windows, aligning with DSM-5’s two-week persistence requirement.
D. Edge Performance
The pipeline sustained real-time processing on commodity hardware while preserving edge feasibility, but interpretation remains constrained by limited and non-naturalistic evaluation data.
- Latency below 1 s per 10 s window was sustained after warm-up on a MacBook Pro M1 under continuous audio load.The benchmark required no GPU acceleration.
- The evaluation used single-session, interview-style DAIC-WOZ speech rather than longitudinal home recordings with naturalistic acoustic conditions.This limits how directly the edge benchmark generalizes to household monitoring.
- The sample size of n=64 limited statistical power, with moderate effect sizes after FDR correction.The reported associations therefore support design rationale without establishing statistical significance.
IV. SUMMARY AND FUTURE WORK
The paper introduces a transparent framework that links acoustic features to DSM-5 indicators while keeping raw speech on-device. Preliminary DAIC-WOZ results support directionally consistent associations, and future work targets broader validation and multimodal clinical use.
- The Linkage Framework exposes which acoustic features support which DSM-5 indicators, enabling symptom-level review.Its edge-first design ensures raw speech never leaves the home.
- Preliminary DAIC-WOZ evaluation found directionally consistent associations for psychomotor change and concentration difficulty.The result supports the framework’s design rationale.
- Future work includes longitudinal home validation, wearable-signal fusion, population calibration across languages and demographics, and clinical integration.The planned extensions retain the framework’s broader monitoring goal.