This project will visualize 3D tongue surface movement during speech from ultrasound, MRI and EPG data. In addition, it will use simple statistical quantities to represent important underlying patterns of tongue behavior during speech. The results will provide normative data for comparison with hearing impaired and other patient populations. Three projects will be executed. The first will reconstruct the movement of 3D tongue surfaces (4D) during speech. We can already reconstruct 3D static surfaces from 2D ultrasound slices. The proposed experiment will develop a time-alignment algorithm, which is the missing piece needed to reconstruct tongue surface movement. These will be the first- ever 4D reconstructions of the tongue. The value of such data is twofold. First, visualization of complex speech movement patterns will allow us to identify and quantify underlying patterns of movement and shape. Second, the images will be useful clinically for comparing normal and disordered patterns, and estimating the underlying musculature. The second project, which is related to the first, will quantify biologically important vocal tract and tongue surface features. The feature extraction will be done using raw ultrasound and MRI data, and also using the 3D and 4D reconstructions. The project will use statistical methods such as augmented principal components analysis, which reduce the dimensionality of a behavior. This will be particularly useful in determining group and individual subject behavior patterns, and removing noise. The components determined for normal groups and individuals can then be used for comparisons to patients. In order to do the first two objectives better, the third project will develop a family of tongue models. The models will use two very different approaches. A biokinematic model will be developed which incorporates the muscular composition of the tongue and reflects the underlying physiology. This will fill gaps in the data when reconstructing the 4D behaviors. The model will be complex enough to optimize its application to the reconstructions, but does not otherwise seek to be a perfect representation of the tongue. The second model will be a parametric statistical model which represents the complexity of 3D tongue behaviors in a simpler way. The two models are expected to converge. For example, principal components of tongue shape, though based on purely mathematical principals, should be explainable by a simplified combination of muscles, and patient deviations should be explainable by some other combination of muscles. Similarly, the biokinematic model should produce tongue surfaces that match the real data and are consistent with the statistical parameters. The biokinematic model also would benefit if the data were statistically manipulated first to reduce noise.

National Institute of Health (NIH)
National Institute on Deafness and Other Communication Disorders (NIDCD)
Research Project (R01)
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Sensory Disorders and Language Study Section (CMS)
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University of Maryland Baltimore
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Pouplier, Marianne (2008) The role of a coda consonant as error trigger in repetition tasks. J Phon 36:114-140
Stone, M; Stock, G; Bunin, K et al. (2007) Comparison of speech production in upright and supine position. J Acoust Soc Am 122:532-41
Parthasarathy, Vijay; Prince, Jerry L; Stone, Maureen et al. (2007) Measuring tongue motion from tagged cine-MRI using harmonic phase (HARP) processing. J Acoust Soc Am 121:491-504
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NessAiver, Moriel; Prince, Jerry L (2003) Visualization of myocardial motion using MICSR trinary checkerboard display. Inf Process Med Imaging 18:573-85

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