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.
Cite as: Wang, J., Kothalkar, P.V., Kim, M., Yunusova, Y., Campbell, T.F., Heitzman, D., Green, J.R. (2016) Predicting Intelligible Speaking Rate in Individuals with Amyotrophic Lateral Sclerosis from a Small Number of Speech Acoustic and Articulatory Samples. Proc. 7th Workshop on Speech and Language Processing for Assistive Technologies (SLPAT 2016), 91-97, doi: 10.21437/SLPAT.2016-16
@inproceedings{wang16_slpat,
author={Jun Wang and Prasanna V. Kothalkar and Myungjong Kim and Yana Yunusova and Thomas F. Campbell and Daragh Heitzman and Jordan R. Green},
title={{Predicting Intelligible Speaking Rate in Individuals with Amyotrophic Lateral Sclerosis from a Small Number of Speech Acoustic and Articulatory Samples}},
year=2016,
booktitle={Proc. 7th Workshop on Speech and Language Processing for Assistive Technologies (SLPAT 2016)},
pages={91--97},
doi={10.21437/SLPAT.2016-16}
}