  As a first step towards a complete computational model of speech learning
involving perception-production loops, we investigate the forward mapping
between pseudo-motor commands and articulatory trajectories. Two phonological
feature sets, based respectively on generative and articulatory phonology, are
used to encode a phonetic target sequence. Different interpolation techniques
are compared to generate smooth trajectories in these feature spaces, with a
potential optimisation of the target value and timing to capture
co-articulation effects. We report the Pearson correlation between a linear
projection of the generated trajectories and articulatory data derived from a
multi-speaker dataset of electromagnetic articulography (EMA) recordings. A
correlation of 0.67 is obtained with an extended feature set based on
generative phonology and a linear interpolation technique. We discuss the
implications of our results for our understanding of the dynamics of biological
motion.
