The RACAD speech corpus of New Brunswick Acadian French: Design and applications
Bibliographic record
Abstract
The RACAD (Reconnaissance automatique de l 'acadien) speech corpus contains high quality audio recordings that can be used to develop recognition systems for the regional varieties of French spoken in the province of New Brunswick, Canada.Its design is informed by linguistic analyses of Acadian French.The corpus contains sentences read by 140 speakers who were selected according to age, gender and geographical region.This paper presents a preliminary application of the corpus in automatic speech recognition research; it outlines an original global monophone recognition model that is designed to handle linguistic variability.Global phone and word recognition rates for this model are satisfactory (about 90%), but they vary considerably across geographical locations.Possible applications of the RACAD corpus in acoustic phonetic and socio-phonetic studies of dialect variation are also described in this paper. R SU M Dans le but de dvelopper des systmes de reconnaissance automatique des varits de franais parles dans la province du Nouveau-Brunswick, au Canada, un corpus d 'enregistrements de haute qualit, le corpus RACAD (Reconnaissance automatique de l 'acadien), a t recueilli.Ce corpus est constitu de phrases lues par 140 locuteurs.Suivant la mthodologie employe dans les tudes linguistiques portant sur le franais acadien, les locuteurs ont t slectionns d 'aprs leur ge, leur sexe et leur appartenance gographique.Cet article dcrit une premire application du processus de reconnaissance automatique de la parole partir de ce corpus; il prsente un modle monophone global qui tient compte de la variabilit linguistique dans le RACAD.Les rsultats montrent que les taux de reconnaissance globale des phones et des mots sont satisfaisants (environ 90%), mais que ces taux varient entre les diverses rgions gographiques.Des applications possibles du RACAD, dans des analyses de phontique acoustique et de sociophontique de la variation rgionale, sont aussi dcrites dans le prsent article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".