Towards Automatic Detection of Developmental Language Disorders

Developmental Language Disorders (DLD) affect over 7% of children in a primary classroom and have lifelong consequences. DLD is typically under-diagnosed due to difficulties in administering and scoring standardised tests that assess their phonological abilities. People with DLD or a related impairment (Dyslexia) have well-recognised issues with phonological processing. We here work with a new dataset from adult speakers: 21 self-declared dyslexics and 30 controls. We focused on two speech elicitation tasks with established links to phonological processing: Non-word Repetition (NWR) and Rapid Automatized Naming (RAN). In NWR, participants hear sets of well-formed non-words (e.g. dipe, vudanuwub, length 1-6 syllables) and repeat each non-word, as accurately as possible. In RAN, participants rapidly name sequences of digits or objects presented in a visual array. We have established a pipeline of automatic approaches to detect errors at the phone and word level and to attempt to classify whether speakers have a DLD or not. Phone level automatic speech recognition with cross-phone error scoring across stimuli and participant responses yielded the lowest error rates, although these remain higher (at 20-26%) than desirable. Aligning continuous articulatory features from the participant’s speech with the stimuli achieved a reasonable detection of when words have been mispronounced (F1 0.826). Classification results were disappointing, with no distinction found between the dyslexic and neuro-typical control participants due to a limitation of the small data set. We plan to apply this pipeline to larger data sets and other diagnostic tasks.