Accessing communication: An analysis of the quality of RTVE’s automatic live subtitles
Bibliographic record
Abstract
Nowadays, deaf and hard-of-hearing viewers should have equal access to audiovisual media and, likewise, the promotion of Galician as a minoritised language should be key for public broadcasters. Therefore, both of them should be compatible, allowing viewers with and without hearing impairments to have access to audiovisual contents in Galician. This paper presents the results of the assessment of the quality of the automatic subtitles being tested in the territorial news programs broadcast by RTVE Spanish TV channel in Galicia and considers the harmonisation of accessibility and standardisation in Galician subtitling. The analysis consisted of 10 five-minute samples in Galician and it was carried out by applying the NER model (Romero-Fresco 2011). The results provide, on the one hand, data on the accuracy of the subtitles and, on the other hand, information on the correction of errors by the speech recognition software used in an attempt to reconcile accessibility and standardisation in Galician subtitling (Romero-Fresco 2021). Finally, the paper concludes with some suggestions for possible improvements to be made to the speech recognition machine in order to accomplish the acceptable threshold of quality and, therefore, provide equal access for deaf and hard-of-hearing viewers.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".