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Improved vocal tract reconstruction and modeling using an image super-resolution technique.

Xinhui Zhou1, Jonghye Woo, Maureen Stone

  • 1Speech Communication Laboratory, Institute of Systems Research and Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland 20742, USA. zxinhui@umd.edu

The Journal of the Acoustical Society of America
|June 8, 2013
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Summary

Integrating multiple MRI scans with super-resolution improves vocal tract visualization and formant prediction accuracy in speech research. This technique enhances speech production modeling by utilizing complementary imaging data.

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Area of Science:

  • Medical Imaging
  • Speech Science
  • Acoustic Phonetics

Background:

  • Magnetic resonance imaging (MRI) is crucial for speech production research.
  • Current methods often use only one MRI image stack, neglecting valuable complementary data.
  • This limits the accuracy of vocal tract models.

Purpose of the Study:

  • To integrate three orthogonal low-resolution MRI stacks into a single isotropic volume using super-resolution.
  • To evaluate the effectiveness of the integrated volume for vocal tract visualization and area function derivation.
  • To assess the accuracy of formant predictions derived from the super-resolution volume.

Main Methods:

  • Application of a novel super-resolution technique to combine sagittal, axial, and coronal MRI stacks.
  • Generation of an isotropic super-resolution volume from low-resolution input data.
  • Derivation of vocal tract area functions from the super-resolution volume.

Main Results:

  • The super-resolution volume provided superior vocal tract visualization compared to individual low-resolution stacks.
  • Derived area functions from the super-resolution volume generally yielded more accurate formant predictions.
  • Formant prediction accuracy was particularly improved for formants sensitive to constrictions in the vocal tract.

Conclusions:

  • Super-resolution integration of multiple MRI stacks enhances vocal tract visualization.
  • This approach improves the accuracy of acoustic modeling in speech production research.
  • Utilizing complementary MRI data via super-resolution offers a significant advancement for speech analysis.