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使用图像超分辨率技术改进声道重建和建模。

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

机构信息

Speech Communication Laboratory, Institute of Systems Research and Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland 20742, USA.

出版信息

J Acoust Soc Am. 2013 Jun;133(6):EL439-45. doi: 10.1121/1.4802903.

Abstract

Magnetic resonance imaging has been widely used in speech production research. Often only one image stack (sagittal, axial, or coronal) is used for vocal tract modeling. As a result, complementary information from other available stacks is not utilized. To overcome this, a recently developed super-resolution technique was applied to integrate three orthogonal low-resolution stacks into one isotropic volume. The results on vowels show that the super-resolution volume produces better vocal tract visualization than any of the low-resolution stacks. Its derived area functions generally produce formant predictions closer to the ground truth, particularly for those formants sensitive to area perturbations at constrictions.

摘要

磁共振成像已广泛应用于言语产生研究。通常仅使用一个图像堆栈(矢状位、轴位或冠状位)进行声道建模。因此,其他可用堆栈的补充信息未被利用。为了克服这一问题,最近开发的超分辨率技术被应用于将三个正交的低分辨率堆栈集成到一个各向同性的体积中。在元音上的结果表明,超分辨率体积产生的声道可视化效果优于任何低分辨率堆栈。其导出的面积函数通常产生更接近真实值的共振峰预测,特别是对于那些对狭窄处面积变化敏感的共振峰。

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