Automatic prediction of intelligible speaking rate for individuals with ALS from speech acoustic and articulatory samples.

ABSTRACT: Purpose: This research aimed to automatically predict intelligible speaking rate for individuals with Amyotrophic Lateral Sclerosis (ALS) based on speech acoustic and articulatory samples. Method: Twelve participants with ALS and two normal subjects produced a total of 1831 phrases. NDI Wave system was used to collect tongue and lip movement and acoustic data synchronously. A machine learning algorithm (i.e. support vector machine) was used to predict intelligible speaking rate (speech intelligibility × speaking rate) from acoustic and articulatory features of the recorded samples. Result: Acoustic, lip movement, and tongue movement information separately, yielded a R² of 0.652, 0.660, and 0.678 and a Root Mean Squared Error (RMSE

SUBMITTER: Wang J 

PROVIDER: S-EPMC6506394 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature