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Updated: Jan 24, 2026
08:32
Ultrasound Images of the Tongue: A Tutorial for Assessment and Remediation of Speech Sound Errors
Published on: January 3, 2017
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Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning
Jonghye Woo1, Fangxu Xing1, Jerry L Prince2
1Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA.
The Journal of the Acoustical Society of America
|June 3, 2019
Summary
This study uses deep learning and magnetic resonance imaging (MRI) to distinguish between healthy and post-cancer tongue muscle coordination. The method achieved 96.90% accuracy, aiding speech motor control research and rehabilitation strategies.
Area of Science:
- Biomedical Engineering
- Speech Science
- Machine Learning
Background:
- Differentiating post-cancer from healthy tongue muscle coordination is crucial for speech motor control theories.
- Developing effective therapeutic and rehabilitative strategies for speech disorders requires accurate classification of tongue muscle patterns.
Purpose of the Study:
- To present a deep learning approach for classifying tongue muscle coordination patterns.
- To differentiate between healthy and post-cancer tongue muscle coordination using magnetic resonance imaging (MRI).
Main Methods:
- Utilized tagged-MRI to track internal tongue tissue points.
- Applied atlas-driven non-negative matrix factorization for dimensionality reduction of deformation fields.
- Employed a convolutional neural network for the classification task.
Main Results:
- Achieved a classification accuracy of 96.90%.
- Demonstrated the effectiveness of the deep learning model in distinguishing between the two groups.
Conclusions:
- The proposed deep learning method shows significant potential for developing therapeutic and rehabilitative strategies for speech-related disorders.
- Accurate classification of tongue muscle coordination patterns can advance speech motor control theories.
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