Detecting Discrepancies Between Subtitles and Audio in Gameplay Videos With EchoTest
Bibliographic record
Abstract
The landscape of accessibility features in video games remains inconsistent, posing challenges for gamers who seek experiences tailored to their needs. Accessibility features, such as subtitles are widely used by players but are difficult to test manually due to the large scope of games and the variability in how subtitles can appear. In this article, we introduce an automated approach (EchoTest) to extract subtitles and spoken audio from a gameplay video, convert them into text, and compare them to detect discrepancies, such as typos, desynchronization, and missing text.EchoTestcan be used by game developers to identify discrepancies between subtitles and spoken audio in their games, enabling them to better test the accessibility of their games. In an empirical study on gameplay videos from 15 popular games,EchoTestcan verify discrepancies between subtitles and audio with a precision of 98% and a recall of 89%. In addition,EchoTestperforms well with a precision of 73% and a recall of 99% on a challenging generated benchmark.
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 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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".