Woo Jonghye, Xing Fangxu, Prince Jerry L, Stone Maureen, Reese Timothy G, Wedeen Van J, El Fakhri Georges
Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Proc SPIE Int Soc Opt Eng. 2020 Feb;11313. Epub 2020 Mar 10.
The tongue is capable of producing intelligible speech because of successful orchestration of muscle groupings-i.e., functional units-of the highly complex muscles over time. Due to the different motions that tongues produce, functional units are transitional structures which transform muscle activity to surface tongue geometry and they vary significantly from one subject to another. In order to compare and contrast the location and size of functional units in the presence of such substantial inter-person variability, it is essential to study both common and subject-specific functional units in a group of people carrying out the same speech task. In this work, a new normalization technique is presented to simultaneously identify the common and subject-specific functional units defined in the tongue when tracked by tagged magnetic resonance imaging. To achieve our goal, a joint sparse non-negative matrix factorization framework is used, which learns a set of building blocks and subject-specific as well as common weighting matrices from motion quantities extracted from displacements. A spectral clustering technique is then applied to the subject-specific and common weighting matrices to determine the subject-specific functional units for each subject and the common functional units across subjects. Our experimental results using tongue motion data show that our approach is able to identify the common and subject-specific functional units with reduced size variability of tongue motion during speech.
舌头能够产生清晰可懂的语音，这是因为随着时间推移，高度复杂的肌肉群（即功能单元）成功地协调运作。由于舌头产生的动作各异，功能单元是将肌肉活动转化为舌面几何形状的过渡结构，而且个体之间差异显著。为了在存在如此大的个体差异的情况下比较和对比功能单元的位置和大小，研究一组执行相同语音任务的人的共同功能单元和个体特有的功能单元至关重要。在这项工作中，提出了一种新的归一化技术，用于在通过标记磁共振成像跟踪舌头时，同时识别在舌头中定义的共同功能单元和个体特有的功能单元。为实现我们的目标，使用了联合稀疏非负矩阵分解框架，该框架从位移提取的运动量中学习一组构建块以及个体特有的和共同的加权矩阵。然后将谱聚类技术应用于个体特有的和共同的加权矩阵，以确定每个个体的个体特有的功能单元以及所有个体的共同功能单元。我们使用舌头运动数据的实验结果表明，我们的方法能够识别出共同功能单元和个体特有的功能单元，同时在语音过程中舌头运动的大小变异性降低。
