Iterative method to estimate muscle activation with a physiological articulatory model

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