Learning new articulator trajectories for a speech production model using artificial neural networks

We present a novel method for generating additional pseudo-articulator trajectories suitable for use within the framework of a stochastically trained speech production system. The system is initialised by inverting a codebook of (articulator, spectral vector) pairs, and the target positions for a set of pseudo-articulators and the mapping from these to speech spectral vectors are then jointly optimised using linearised Kalman filtering and an assembly of neural networks. A separate network is then used to hypothesise a new articulator trajectory as a function of the existing articulators and the output error of the system. The techniques used to initialise and train the system are described and preliminary results for the generation of new pseudo-articulatory inputs are presented