  The way infants use auditory cues to learn to speak despite the acoustic
mismatch of their vocal apparatus is a hot topic of scientific debate. The
simulation of early vocal learning using articulatory speech synthesis offers a
way towards gaining a deeper understanding of this process. One of the crucial
parameters in these simulations is the choice of features and a metric to
evaluate the acoustic error between the synthesised sound and the reference
target. We contribute with evaluating the performance of a set of 40
feature-metric combinations for the task of optimising the production of static
vowels with a high-quality articulatory synthesiser. Towards this end we assess
the usability of formant error and the projection of the feature-metric error
surface in the normalised F1-F2 formant space. We show that this approach can
be used to evaluate the impact of features and metrics and also to offer
insight to perceptual results.
