Deep learning-driven phonetic profiling of dysarthric speech

Dysarthric speech poses significant challenges for clinical assessment and diagnosis due to wide variability in the nature and severity of speech impairments associated with it. To address this, this study investigates the phonetic variations in different types of dysarthric speech using the deep learning model Phonet. Recordings of the word “Catastrophe” from 54 patients diagnosed with dysarthria were analyzed to extract posterior probabilities of consonants and vowels. The analysis revealed distinct posterior probability patterns for consonants and vowels across three types of dysarthria, indicating disorder-specific articulatory-based speech characteristics. Moreover, within each dysarthric group, variations in posterior probabilities aligned with the severity of rated articulation difficulty, reflecting nuanced intra-disease phonetic differences. These findings demonstrate Phonet’s ability to capture subtle speech variations and link them to both dysarthria type and severity. By quantifying these phonetic alterations, this study offers a novel and robust framework for objectively evaluating articulation difficulties in dysarthric speech. The results emphasize the clinical potential of Phonet as an advanced tool for improving the diagnosis and monitoring of dysarthria, paving the way for innovative applications in speech-based clinical assessments.