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Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurological disease that affects the speech motor functions, resulting in dysarthria, a motor speech disorder. Speech and articulation deterioration is an indicator of the disease progression of ALS; timely monitoring of the disease progression is critical for clinical management of these patients. This paper investigated machine prediction of intelligible speaking rate of nine individuals with ALS based on a small number of speech acoustic and articulatory samples. Two feature selection techniques - decision tree and gradient boosting - were used with support vector regression for predicting the intelligible speaking rate. Experimental results demonstrated the feasibility of predicting intelligible speaking rate from only a small number of speech samples. Furthermore, adding articulatory features to acoustic features improved prediction performance, when decision tree was used as the feature selection technique.

europe pmc (pubmed central)

Predicting Intelligible Speaking Rate in Individuals with Amyotrophic Lateral Sclerosis from a Small Number of Speech Acoustic and Articulatory Samples