Bibliographic record
Abstract
CL-MASR Dataset This is the dataset used in the continual learning for multilingual ASR (CL-MASR) benchmark. It is composed of speech recordings from 20 languages selected from the Common Voice 13 dataset. For each language, it includes up to 10/1/1 hours for train/dev/test, respectively. The CL-MASR benchmark platform is available in the SpeechBrain toolkit (see recipes/CommonVoice): https://github.com/speechbrain/speechbrain The original Common Voice 13 data are available at: https://commonvoice.mozilla.org/en/datasets List of Languages - English (en) - Chinese (zh-CN) - German (de) - Spanish (es) - Russian (ru) - French (fr) - Portuguese (pt) - Japanese (ja) - Turkish (tr) - Polish (pl) - Kinyarwanda (rw) - Esperanto (eo) - Kabyle (kab) - Luganda (lg) - Meadow Mari (mhr) - Central Kurdish (ckb) - Abkhaz (ab) - Kurmanji Kurdish (kmr) - Frisian (fy-NL) - Interlingua (ia)
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.007 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.142 |
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".