Subject-specific biomechanical modelling of the oropharynx: towards speech production. Issue 6 (2nd November 2017)

• Record Type: Journal Article
• Authors: Harandi, Negar Mohaghegh; Stavness, Ian; Woo, Jonghye; Stone, Maureen; Abugharbieh, Rafeef; Fels, Sidney
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 5:Issue 6(2017)
• Page Start: 416
• Page End: 426
• Publication Date: 2017-11-02
• Subjects: subject-specific modelling -- inverse simulation -- oropharynx -- speech production -- skinning; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; 616.0757
• DOI: 10.1080/21681163.2015.1033756
• Languages: English
• ISSNs: 2168-1163

Abstract

Abstract : 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 coupled 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.