Locally-Weighted Regression for Estimating the Forward Kinematics of a Geometric Vocal Tract Model

摘要

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. articu-latory), a faithful forward model will be complex and infeasi-ble 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.