Assessing and
Treating Speech Motor Impairment:
A Machine Learning
Approach
Jun
Wang, PhD
Callier
Center for Communicative Disorders
University
of Texas – Dallas
Thursday,
February 28, 2013 at 11:00 in Room C141 Speech & Hearing
Although there is a critical need for
objectively assessing the speech motor decline due to neurological diseases
(e.g., amyotrophic lateral sclerosis, ALS) and for assistive technologies for
people with speech impairment, few options are available due to the logistical
difficulty of tongue movement recording. This research investigated novel
approaches for assessment and treatment for speech motor impairment, which
leverage the recent advances of 3D motion capture technologies and the power of
machine learning algorithms for data analysis. Machine learning classifiers
(e.g., support vector machine and Procrustes analysis) were used to classify
and quantify the articulatory distinctiveness of phonemes, words, and sentences
based on tongue and lip movement time-series data. A novel measure,
articulatory vowel space area, was derived and used for assessing the impaired
speech motor control due to ALS. In addition, an online word and sentence
recognition algorithm from continuous articulatory movement data was evaluated.
Experimental results showed the potential of the machine learning algorithms
for analyzing tongue movements for speech assessment and for the development of
a silent speech recognition system that generates speech in response to
silently produced articulatory movements.
