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Subject-specific biomechanical modelling of the oropharynx: towards speech production

机译:口咽的主题特定生物力学建模:走向语音产生

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摘要

Biomechanical models of the oropharynx are beneficial to treatment planning of speech impediments by providing valuable insight into the speech function such as motor control. In this paper, we develop a subject-specific model of the oropharynx and investigate its utility in speech production. Our approach adapts a generic tongue-jaw-hyoid model [Stavness I, Lloyd JE, Payan Y, Fels S. 2011. Coupled hard-soft tissue simulation with contact and constraints applied to jaw-tongue-hyoid dynamics. Int J Numer Method Biomed Eng. 27(3):367-390] to fit and track dynamic volumetric MRI data of a normal speaker, subsequently couplcd to a source-filter-based acoustic synthesiser. We demonstrate our model's ability to track tongue tissue motion, simulate plausible muscle activation patterns, as well as generate acoustic results that have comparable spectral features to the associated recorded audio. Finally, we propose a method to adjust the spatial resolution of our subject-specific tongue model to match the fidelity level of our MRI data and speech synthesiser. Our findings suggest that a higher resolution tongue model - using similar muscle fibre definition - does not show a significant improvement in acoustic performance, for our speech utterance and at this level of fidelity; however, we believe that our approach enables further refinements of the muscle fibres suitable for studying longer speech sequences and finer muscle innervation using higher resolution dynamic data.
机译:口咽的生物力学模型通过提供对语音功能(例如运动控制)的宝贵见解,有助于语音障碍的治疗计划。在本文中,我们开发了口咽的特定主题模型,并研究了其在语音产生中的效用。我们的方法适用于普通的下颌舌骨舌骨模型[Stavness I,Lloyd JE,Payan Y,Fels S.2011。结合接触和约束的硬-软组织模拟应用于颌舌舌骨舌骨动力学。 Int J Numer方法生物医学工程。 27(3):367-390]来拟合和跟踪普通说话者的动态体积MRI数据,随后将其耦合到基于源滤波器的声学合成器。我们证明了我们的模型具有跟踪舌头组织运动,模拟可能的肌肉激活模式以及生成声学结果的能力,这些声学结果具有与相关录制的音频相当的频谱特征。最后,我们提出一种方法来调整我们特定对象的舌头模型的空间分辨率,以匹配我们的MRI数据和语音合成器的保真度。我们的研究结果表明,使用类似的肌肉纤维定义的高分辨率舌头模型,对于我们的言语表达和这种保真度,在声学性能上并未显示出明显的改善。但是,我们相信,我们的方法可以使肌肉纤维进一步细化,以使用更长的动态数据来研究更长的语音序列和更精细的神经支配。

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