Article  |   October 2013
Articulatory Distinctiveness of Vowels and Consonants: A Data-Driven Approach
Author Affiliations & Notes
  • Jun Wang
    University of Nebraska—Lincoln
  • Jordan R. Green
    University of Nebraska—Lincoln
  • Ashok Samal
    University of Nebraska—Lincoln
  • Yana Yunusova
    University of Toronto, Toronto, Ontario, Canada
  • Correspondence to Jun Wang, who is now at Callier Center for Communication Disorders, University of Texas at Dallas: wangjun@utdallas.edu
  • Jordan R. Green is now at MGH Institute of Health Professions, Boston, MA
    Jordan R. Green is now at MGH Institute of Health Professions, Boston, MA×
  • Editor: Jody Kreiman
    Editor: Jody Kreiman×
  • Associate Editor: Ben A. M. Maassen
    Associate Editor: Ben A. M. Maassen×
Article Information
Speech, Voice & Prosody / Speech
Article   |   October 2013
Articulatory Distinctiveness of Vowels and Consonants: A Data-Driven Approach
Journal of Speech, Language, and Hearing Research, October 2013, Vol. 56, 1539-1551. doi:10.1044/1092-4388(2013/12-0030)
History: Received January 22, 2012 , Revised June 27, 2012 , Accepted February 20, 2013
Journal of Speech, Language, and Hearing Research, October 2013, Vol. 56, 1539-1551. doi:10.1044/1092-4388(2013/12-0030)
History: Received January 22, 2012; Revised June 27, 2012; Accepted February 20, 2013

Purpose: To quantify the articulatory distinctiveness of 8 major English vowels and 11 English consonants based on tongue and lip movement time series data using a data-driven approach.

Method: Tongue and lip movements of 8 vowels and 11 consonants from 10 healthy talkers were collected. First, classification accuracies were obtained using 2 complementary approaches: (a) Procrustes analysis and (b) a support vector machine. Procrustes distance was then used to measure the articulatory distinctiveness among vowels and consonants. Finally, the distance (distinctiveness) matrices of different vowel pairs and consonant pairs were used to derive articulatory vowel and consonant spaces using multidimensional scaling.

Results: Vowel classification accuracies of 91.67% and 89.05% and consonant classification accuracies of 91.37% and 88.94% were obtained using Procrustes analysis and a support vector machine, respectively. Articulatory vowel and consonant spaces were derived based on the pairwise Procrustes distances.

Conclusions: The articulatory vowel space derived in this study resembled the long-standing descriptive articulatory vowel space defined by tongue height and advancement. The articulatory consonant space was consistent with feature-based classification of English consonants. The derived articulatory vowel and consonant spaces may have clinical implications, including serving as an objective measure of the severity of articulatory impairment.

Acknowledgments
This work was funded in part by the Barkley Trust, Barkley Memorial Center, University of Nebraska—Lincoln, and National Institutes of Health Grant R01 DC009890/DC/NIDCD NIH HHS. We thank Tom D. Carrell, Mili Kuruvilla, Lori Synhorst, Cynthia Didion, Rebecca Hoesing, Kayanne Hamling, Katie Lippincott, and Kelly Veys for their contribution to participant recruitment, data collection, and data processing.
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