  We estimate articulatory movements in speech production from different
modalities - acoustics and phonemes. Acoustic-to articulatory inversion (AAI)
is a sequence-to-sequence task. On the other hand, phoneme to articulatory
(PTA) motion estimation faces a key challenge in reliably aligning the text and
the articulatory movements. To address this challenge, we explore the use of a
transformer architecture - FastSpeech, with explicit duration modelling to
learn hard alignments between the phonemes and articulatory movements. We also
train a transformer model on AAI. We use correlation coefficient (CC) and root
mean squared error (rMSE) to assess the estimation performance in comparison to
existing methods on both tasks. We observe 154%, 11.8% & 4.8% relative
improvement in CC with subject-dependent, pooled and fine-tuning strategies,
respectively, for PTA estimation. Additionally, on the AAI task, we obtain
1.5%, 3% and 3.1% relative gain in CC on the same setups compared to the
state-of-the-art baseline. We further present the computational benefits of
having transformer architecture as representation blocks.
