  We address the problem of reconstructing articulatory movements, given audio
and/or phonetic labels. The scarce availability of multi-speaker articulatory
data makes it difficult to learn a reconstruction that generalizes to new
speakers and across datasets. We first consider the XRMB dataset where audio,
articulatory measurements and phonetic transcriptions are available. We show
that phonetic labels, used as input to deep recurrent neural networks that
reconstruct articulatory features, are in general more helpful than acoustic
features in both matched and mismatched training-testing conditions. In a
second experiment, we test a novel approach that attempts to build articulatory
features from prior articulatory information extracted from phonetic labels.
Such approach recovers vocal tract movements directly from an acoustic-only
dataset without using any articulatory measurement. Results show that
articulatory features generated by this approach can correlate up to 0.59
Pearson product-moment correlation with measured articulatory features.
