Sampling of natural speech for the assessment of psychopathology: data collection procedure and inter-rater reliability
Bibliographic record
Abstract
OBJECTIVES: Recent research explores speech as a marker of psychiatric illness. Applications of speech analysis in clinical settings depend on reliable methods of speech data collection and processing, yet no standard exists thus far. We present a speech sampling and coding procedure developed to standardize methods across literature and facilitate assessment of psychiatric features through speech. METHODS: We developed a procedure to collect naturalistic speech samples from adults using emotionally-valenced prompting. Speech variables characterize features of illness that can aid in assessment of psychopathology. The samples are transcribed, segmented, and coded by human raters on variables indexing speaker, reference, emotions, and features of psychopathology. RESULTS: Samples from 200 adult participants were analyzed. The protocol elicited an average of 10 minutes of speech per assessment. Almost all participants provided 5 minutes or more of speech suitable for analysis. Human coders identified segment-level sentiment, reference, and emotions with high inter-rater reliability (ICC 0.60 to 0.79), but agreed less consistently on more complex features, such as worry and rumination (ICC 0.24 to 0.47). CONCLUSION: We present a speech collection and processing method that elicits speech samples from participants with a range of psychopathology, suitable for manual and computerized analysis with applications to clinical settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.066 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".