Locally-weighted regression for estimating the forward kinematics of a geometric vocal tract model

Task-space control is well studied in modeling speech production [1, 2, 3, 4]. Implementing control of this kind requires an accurate kinematic forward model. Despite debate about how to define the tasks for speech (i.e., acoustical vs. articulatory), a faithful forward model will be complex and infeasible to express analytically. Thus, it is necessary to learn the forward model from data. Artificial Neural Networks (ANNs) have previously been suggested for this [3, 4, 6]. We argue for the use of locally-linear methods, such as Locally-Weighted Regression (LWR). While ANNs are capable of learning complex forward maps, LWR is more appropriate. Common formulations of control assume locally-linearity, whereas ANNs fit a nonlinear model to the entire map. Likewise, training LWR is simple compared to the complex optimization for ANNs. We provide an empirical comparison of these methods for learning a vocal tract forward model, discussing theoretical and practical aspects of each.