Herramientas informáticas disponibles para la automatización de la traducción audiovisual1 (“revoicing”)
Bibliographic record
Abstract
Cet présent article fait état d’une évaluation des logiciels informatiques utilisés dans les principales modalités de la traduction audiovisuelle impliquant la reformulation orale de la traduction du texte cible (ce que l’on appelle en anglais revoicing ) : l’audio-description, la voix superposée ( voice-over ) et le doublage. Cette dernière modalité est celle qui nous intéresse le plus, car il existe très peu de logiciels spécialisés la concernant. Après l’examen de ces logiciels, nous proposons deux options d’automatisation facilitant le travail des traducteurs en situation de doublage. La dernière phase de programmation de la première option est actuellement en cours. Puis, dans la dernière partie de cet article, nous proposons un glossaire et une liste de logiciels informatiques destinés au traducteur audiovisuel qui sont disponibles sur le marché.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".