Towards a Reliable French Speech Recognition Tool for an Automated Diagnosis of Learning Disabilities
Bibliographic record
Abstract
Dyslexia, characterized by severe challenges in reading and spelling acquisition, presents a substantial barrier to proficient literacy, resulting in significantly reduced reading speed (2 to 3 times slower) and diminished text comprehension. With a prevalence ranging from 5G to 10%in the population, early intervention by speech and language pathologists (SLPs) can mitigate dyslexia's effects, but the diagnosis bottleneck impedes timely support. To address this, we propose leveraging machine learning tools to expedite the diagnosis process, focusing on automating phonetic transcription, a critical step in dyslexia assessment. We investigated the practicality of two model configurations utilizing Google's speech-to-text API with children speech in evaluation scenarios and compared their results against transcriptions crafted by experts. The first configuration focuses on Google API's speech-to-text while the second integrates Phonemizer, a text-to-phonemes tool based on a dictionary. Results analysis indicate that our Google-Phonemizer model yields reading accuracies comparable to those computed from human-made transcriptions, offering promise for clinical application. These findings underscore the potential of AI-driven solutions to enhance dyslexia diagnosis efficiency, paving the way for improved accessibility to vital SLP services.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".