Automatic live captioning in simultaneous interpreting: A comparative analysis of quality
Bibliographic record
Abstract
The increasing availability of automatic live captioning (ALC) technology offers new possibilities for its application in simultaneous interpreting (SI). However, its effectiveness in SI contexts requires further investigation, particularly compared to conventional SI modes. This study addresses this gap by conducting a comparative analysis of four interpreting modes: SI without text, SI with text, sight translation and SI with ALC. We evaluated the performance of 27 interpreter trainees from three perspectives: human raters’ assessment of interpreting quality, interpreters’ self-perceived performance and local interpreting quality in relation to specific problem triggers and automatic speech recognition (ASR) errors. Results indicate that interpreters in SI with ALC perform at an intermediate quality level, surpassing SI without text, but not matching the quality achieved in SI with text or sight translation. Our analysis of problem triggers and ASR errors reveals a nuanced relationship between technological assistance and interpreter performance, highlighting both the benefits and challenges of SI with ALC. This research contributes to the growing body of knowledge on SI with ALC by examining the specific role and impact of ALC in SI, offering insights for computer-assisted interpreting development and suggesting potential applications in interpreter training programs.
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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 | 0.020 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".