Towards a reliable french speech recognition tool for an automated diagnosis of learning disabilities

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.

Mots clés

automatic speech recognition; reliability; phoneme recognition; learning disabilities; dyslexia