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High-Resolution Non-Invasive Imaging of Upper Vocal Tract Articulators Compatible with Human Brain Recordings

机译:高分辨率无创成像与人脑记录兼容的上声带咬合架

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摘要

A complete neurobiological understanding of speech motor control requires determination of the relationship between simultaneously recorded neural activity and the kinematics of the lips, jaw, tongue, and larynx. Many speech articulators are internal to the vocal tract, and therefore simultaneously tracking the kinematics of all articulators is nontrivial—especially in the context of human electrophysiology recordings. Here, we describe a noninvasive, multi-modal imaging system to monitor vocal tract kinematics, demonstrate this system in six speakers during production of nine American English vowels, and provide new analysis of such data. Classification and regression analysis revealed considerable variability in the articulator-to-acoustic relationship across speakers. Non-negative matrix factorization extracted basis sets capturing vocal tract shapes allowing for higher vowel classification accuracy than traditional methods. Statistical speech synthesis generated speech from vocal tract measurements, and we demonstrate perceptual identification. We demonstrate the capacity to predict lip kinematics from ventral sensorimotor cortical activity. These results demonstrate a multi-modal system to non-invasively monitor articulator kinematics during speech production, describe novel analytic methods for relating kinematic data to speech acoustics, and provide the first decoding of speech kinematics from electrocorticography. These advances will be critical for understanding the cortical basis of speech production and the creation of vocal prosthetics.
机译:对语音运动控制的完整神经生物学理解需要确定同时记录的神经活动与嘴唇,下颌,舌头和喉的运动学之间的关系。许多语音发音器位于声道内部,因此同时跟踪所有发音器的运动学并不是一件容易的事,尤其是在人类电生理学记录的背景下。在这里,我们描述了一种非侵入性的多模态成像系统,用于监控声道运动学,在生产九个美国英语元音的过程中,在六个说话者中演示该系统,并提供此类数据的新分析。分类和回归分析表明,说话人的发音器与声音之间的关系存在很大差异。非负矩阵分解提取的基集可捕获声道形状,从而比传统方法具有更高的元音分类精度。统计语音合成从声道测量生成语音,并且我们展示了感知识别。我们证明了从腹感觉运动皮层活动预测唇运动学的能力。这些结果证明了一种多模态系统,可在语音产生过程中无创地监测咬合架运动学,描述了将运动学数据与语音声学相关联的新颖分析方法,并提供了从电皮层照相术对语音运动学的首次解码。这些进步对于理解言语产生的皮质基础和声假肢的创造至关重要。

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