Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning
Date
2019Journal
Journal of the Acoustical Society of AmericaPublisher
Acoustical Society of AmericaType
Article
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The ability to differentiate post-cancer from healthy tongue muscle coordination patterns is necessary for the advancement of speech motor control theories and for the development of therapeutic and rehabilitative strategies. A deep learning approach is presented to classify two groups using muscle coordination patterns from magnetic resonance imaging (MRI). The proposed method uses tagged-MRI to track the tongue's internal tissue points and atlas-driven non-negative matrix factorization to reduce the dimensionality of the deformation fields. A convolutional neural network is applied to the classification task yielding an accuracy of 96.90%, offering the potential to the development of therapeutic or rehabilitative strategies in speech-related disorders. Copyright 2019 Acoustical Society of America.Identifier to cite or link to this item
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10.1121/1.5103191
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