Thesis: Ph. D. in Biomedical Engineering, Harvard-MIT Program in Health Sciences and Technology, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 213-227). / Progressive bulbar motor deterioration resulting from amyotrophic lateral sclerosis (ALS) leads to speech impairment. Despite the devastating consequences of speech impairment to life quality, few options are available to objectively assess speech motor involvement. The overarching goal of this research was to derive objective measures of speech acoustics that can be used to support clinical decision making. To achieve this goal, we obtained 121 speech samples from 33 patients with ALS who repeated the phrase "Buy Bobby a puppy" five times in succession. In total, 342 acoustic features were semi-automatically extracted from each speech recording. Pearson correlations were computed between each feature and three metrics of overall speech severity: sentence intelligibility, speaking rate, and communication efficiency. The findings were grounded within a physiologic framework where acoustic features were grouped into one of three domains that when combined, were hypothesized to broadly characterize articulatory performance: articulatory specification, articulatory coupling, and articulatory consistency. To obtain the most accurate prediction of ALS with the features we extracted, we compared two machine learning algorithms: linear regression and random forest. In shuffle-split cross-validation, the strongest mean Pearson correlations we obtained between actual and predicted intelligibility, speaking rate, and communication efficiency were 0.67, 0.74, and 0.77, respectively (SD=0.077, 0.050, and 0.059, respectively). Of the three domains, the specificity features were the most strongly associated with intelligibility impairments (mean r=0.68), and coupling was the most strongly associated with slower speaking rate (mean r=0.73). Specificity and coupling yielded similar performances in communication efficiency prediction. Other contributions of this thesis are that it is the first to implement a framework of dysarthric speech in terms of three domains: specification, coupling, and consistency; the first to validate automated formant tracking in dysarthric speech; and the first to perform an in-depth investigation into physiologically-inspired acoustic features that describe articulatory impairments of patients with ALS. Novel findings include the presence of abnormal formant coupling patterns, which may suggest greater tonguejaw coupling, in patients with more severe dysarthria due to ALS. Areas of future research involve further feature discovery, improved analysis methods, and a deeper understanding of relations to articulatory kinematics. / by Rachelle L. Horwitz-Martin. / Ph. D. in Biomedical Engineering
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/113789 |
| Date | January 2017 |
| Creators | Horwitz-Martin, Rachelle L. (Rachelle Laura) |
| Contributors | Thomas F. Quatieri and Jordan R. Green., Harvard--MIT Program in Health Sciences and Technology., Harvard--MIT Program in Health Sciences and Technology. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 227 pages, application/pdf |
| Rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission., http://dspace.mit.edu/handle/1721.1/7582 |
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