  Most organisms including humans function by coordinating and integrating
sensory signals with motor actions to survive and accomplish desired tasks.
Learning these complex sensorimotor mappings proceeds simultaneously and often
in an unsupervised or semi-supervised fashion. An autoencoder architecture
(MirrorNet) inspired by this sensorimotor learning paradigm is explored in this
work to control an articulatory synthesizer, with minimal exposure to
ground-truth articulatory data. The articulatory synthesizer takes as input a
set of six vocal Tract Variables (TVs) and source features (voicing indicators
and pitch) and is able to synthesize continuous speech for unseen speakers. We
show that the MirrorNet, once initialized (with ~30 mins of articulatory data)
and further trained in unsupervised fashion (`learning phase'), can learn
meaningful articulatory representations with comparable accuracy to
articulatory speech-inversion systems trained in a completely supervised
fashion.
