  Unlike phoneme sequences, movements of speech articulators (lips, tongue,
jaw, velum) and the resultant acoustic signal are known to encode not only the
linguistic message but also carry para-linguistic information. While several
works exist for estimating articulatory movement from acoustic signals, little
is known to what extent articulatory movements can be predicted only from
linguistic information, i.e., phoneme sequence. In this work, we estimate
articulatory movements from three different input representations: R1) acoustic
signal, R2) phoneme sequence, R3) phoneme sequence with timing information.
While an attention network is used for estimating articulatory movement in the
case of R2, BLSTM network is used for R1 and R3. Experiments with ten subjects'
acoustic-articulatory data reveal that the estimation techniques achieve an
average correlation coefficient of 0.85, 0.81, and 0.81 in the case of R1, R2,
and R3 respectively. This indicates that attention network, although uses only
phoneme sequence (R2) without any timing information, results in an estimation
performance similar to that using rich acoustic signal (R1), suggesting that
articulatory motion is primarily driven by the linguistic message. The
correlation coefficient is further improved to 0.88 when R1 and R3 are used
together for estimating articulatory movements.
