Fostering human rights through TalkBank
Bibliographic record
Abstract
In accord with articles 19 and 27 of the Universal Declaration of Human Rights, people with speech and language disorders have the right to receive maximal benefit from academic research on speech and language acquisition and disorders. To evaluate the diverse nature of speech and language disorders, this research must have access to large datasets, as well as to refined tools for the systematic analysis of these datasets. The TalkBank system addresses this need by providing researchers with thousands of hours of open-access database archives of digital audio, video and transcript files documenting typical and disordered language use in dozens of languages and cultures. In this paper, we review the TalkBank system, with an emphasis on the AphasiaBank, PhonBank and FluencyBank databases. We describe how specialised assessment tools can be used to study issues in speech and language acquisition and disorders recorded within these databases. We then provide illustrations of how assessments support the needs of researchers, clinicians, developers, and educators, whose combined work contributes solutions for people with speech, language and language learning disorders worldwide.
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 | 0.078 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.026 |
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".