Automated Assessment of Oral Diadochokinesis in Multiple Sclerosis Using a Neural Network Approach: Effect of Different Syllable Repetition Paradigms

Slow and irregular oral diadochokinesis represents an important manifestation of spastic and ataxic dysarthria in multiple sclerosis (MS). We aimed to develop a robust algorithm based on convolutional neural networks for the accurate detection of syllables from different types of alternating motion rate (AMR) and sequential motion rate (SMR) paradigms. Subsequently, we explored the sensitivity of AMR and SMR paradigms based on voiceless and voiced consonants in the detection of speech impairment. The four types of syllable repetition paradigms including /ta/, /da/, /pa/-/ta/-/ka/, and /ba/-/da/-/ga/ were collected from 120 MS patients and 60 matched healthy control speakers. Our neural network algorithm was able to correctly identify the position of individual syllables with a very high average accuracy of 97.8%, with the correct temporal detection of syllable position of 87.8% for 10 ms and 95.5% for 20 ms tolerance value. We found significantly altered diadochokinetic rate and regularity in MS compared to controls across all types of investigated tasks (p <; 0.001 ). MS patients showed slower speech for SMR compared to AMR tasks, whereas voiced paradigms were more irregular. Objective evaluation of oral diadochokinesis using different AMR and SMR paradigms may provide important information regarding speech severity and pathophysiology of the underlying disease.