La transcription perceptuelle au service du corpus de conversations naturelles
Bibliographic record
Abstract
Un des principaux obstacles à l’analyse acoustique informatisée des conversations naturelles demeure la présence de chevauchements de parole et de conversations parallèles, où se trouve, selon nos résultats, la moitié des manifestations prosodiques réalisées en contexte naturel. Pour ne pas nous priver de cet aspect inhérent à la conversation, nous préférons recueillir nos données primaires en contexte naturel et non pas en laboratoire. Or les nombreux signaux de parole mixte qui en résultent ne peuvent être soumis à une analyse acoustique informatisée. Nous proposons donc de nous en remettre entièrement à une analyse humaine du matériel verbal et paraverbal, dans une démarche favorisant la transcription pertinente des indices de la prosodie et un niveau de fidélité aux données primaires qui soit satisfaisant pour des analyses lexico-sémantiques et pragmatiques (de type interactionnel) de la conversation. Nous proposons également certaines dispositions graphiques pour augmenter la lisibilité de telles transcriptions.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".