Iterative method to estimate muscle activation with a physiological articulatory model

Abstract

Computational modeling of the speech organs is able to improve our understanding of human speech motor control. In order to investigate muscle activation in speech motor control, we have developed an automatic estimation method based on a 3D physiological articulatory model. In this method, the articulatory target was defined by the entire posture of the tongue and jaw in the midsagittal plane, which was reduced to a six-dimensional space by principal component analysis (PCA). In the PCA space, the distance between an articulatory target and the model was gradually minimized by automatically adjusting muscle activations. The adjustment of muscle activations was guided by a dynamic PCA workspace that was used to predict individual muscle functions in a given position. This dynamic PCA workspace was estimated on the basis of an interpolation of eight reference PCA workspaces. The proposed method was assessed by estimating muscle activations for five Japanese vowel postures that were extracted from magnetic resonance images. The results showed that the proposed method can generate muscle activation patterns that can control the model to realize given articulatory targets. In addition, the estimated muscle activation patterns were consistent with anatomical knowledge and previously reported measurement data.

Using a Biomechanical Model and Articulatory Data for the Numerical Production of Vowels

“…Payan [6] presented a 2D biomechanical tongue model to synthesize vowel-vowel sequences; Gérard [7] used a 3D biomechanical tongue model to study speech motor control and Buchaillard [8] used a 3D tongue biomechanical model for cardinal vowel production. Other 3D biomechanical models were developed by Wu [9], Fang [10] and Stavness [11] to study muscle activation of tongue and jaw. Most recently Anderson [12] introduced a comprehensive biomechanical model of oropharyngeal structures.…”

Towards a predictive model for functional loss after oral cancer treatment

“…However, finding the patientspecific muscular activation signals, needed for the biomechanical models, is difficult. [22] As an alternative, the current study describes an empirically derived model that is able to estimate the dynamics of lip displacements with an average RMS error of 2.43 mm. This empirical model is sEMG driven, which incorporates patient-specific information.…”

“…Oral and oropharyngeal squamous cell carcinoma together rank sixth among the most common types of cancer [22,23], with annual incidences of approximately 300,000 for lip and oral cavity cancers, and 142,000 for pharyngeal cancers (excluding the nasopharynx) worldwide. [24] And numbers are rising.…”

“…[34][35][36] Additional risk factors are smokeless tobacco and betel quid chewing [37,38], as well as working environment [39,40], and in lip cancer ultraviolet light exposure. [22] As mentioned above, another risk factor for oropharyngeal cancer is HPV [22,24], [31,41,42], which mainly affects the younger age group. Tonsil cancer, in particular, is quite evidently correlated with HPV.…”

“…Function loss in turn may lead to other problems like depression and deficient intake. [3,22] Several studies have analysed consequences of surgery for speech [4,73,74], swallowing [75,76], or both [77]. Langendijk et al evaluated swallowing dysfunction after treatment with either radiotherapy or chemoradiation [78]; Weber et al studied multiple functional outcomes after surgery and/or chemoradiation [3]; and Dwivedi et al reviewed literature on speech outcomes after any kind of treatment for oral cavity and oropharyngeal cancer [72].…”