Design of tool for analysis of speech development disorders using landmarks and other acoustic cues
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Stefanie Shattuck-Hufnagel and Jeung-Yoon Elizabeth Choi.
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Non-word repetition tasks have been used to diagnose children with various developmental difficulties with phonology, but these productions have not been phonetically analyzed to reveal the nature of the modifications produced by children diagnosed with SLI, autism spectrum disorder or dyslexia compared to those produced by typically-developing children. In this thesis, we compared the modification of predicted acoustic cues to distinctive features of manner, place and voicing for just under 30 children (ages 5-12), for the CN-Rep word inventory, in an extension of the earlier analysis in Levy et al. 2014. Feature cues, including abrupt acoustic landmarks (Stevens 2002) and other acoustic feature cues, were hand-labeled and analysis of factors that may influence feature cue modifications included position in the word, position in the syllable, word length measured in syllables, lexical stress, and manner type. Results suggest specific patterns of modification in specific contexts for specific clinical populations. These findings set the foundation for understanding how phonetic variation in speech arises in both typical and clinical populations, and for using this knowledge to develop tools to aid in more accurate and insightful diagnosis as well as improved intervention methods.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (page 71).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.