On the Assessment of Stability and Patterning of Speech Movements Speech requires the control of complex movements of orofacial structures to produce dynamic variations in the vocal tract transfer function. The nature of the underlying motor control processes has traditionally been investigated by employing measures of articulatory movements, including movement amplitude, velocity, and duration, at selected points in time. An ... Research Note
Research Note  |   February 01, 2000
On the Assessment of Stability and Patterning of Speech Movements
 
Author Affiliations & Notes
  • Anne Smith
    Purdue University West Lafayette, IN
  • Michael Johnson
    Purdue University West Lafayette, IN
  • Clare McGillem
    Purdue University West Lafayette, IN
  • Lisa Goffman
    Purdue University West Lafayette, IN
  • Corresponding author: e-mail: asmith@purdue.edu
Article Information
Speech, Voice & Prosody / Speech / Research Note
Research Note   |   February 01, 2000
On the Assessment of Stability and Patterning of Speech Movements
Journal of Speech, Language, and Hearing Research, February 2000, Vol. 43, 277-286. doi:10.1044/jslhr.4301.277
History: Received July 28, 1998 , Accepted July 6, 1999
 
Journal of Speech, Language, and Hearing Research, February 2000, Vol. 43, 277-286. doi:10.1044/jslhr.4301.277
History: Received July 28, 1998; Accepted July 6, 1999

Speech requires the control of complex movements of orofacial structures to produce dynamic variations in the vocal tract transfer function. The nature of the underlying motor control processes has traditionally been investigated by employing measures of articulatory movements, including movement amplitude, velocity, and duration, at selected points in time. An alternative approach, first used in the study of limb motion, is to examine the entire movement trajectory over time. A new approach to speech movement trajectory analysis was introduced in earlier work from this laboratory. In this method, trajectories from multiple movement sequences are time- and amplitude-normalized, and the STI (spatiotemporal index) is computed to capture the degree of convergence of a set of trajectories onto a single, underlying movement template. This research note describes the rationale for this analysis and provides a detailed description of the signal processing involved. Alternative interpolation procedures for time-normalization of kinematic data are also considered.

Acknowledgments
This work was supported by National Institutes of Health (NIDCD) grants DC00559 and DC02527. Our colleague Claire McGillem passed away during the final revision of this paper. We acknowledge with deep gratitude Claire’s long-term and insightful contributions to our work.
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