  Acoustic-to-articulatory inversion (AAI) methods estimate articulatory
movements from the acoustic speech signal, which can be useful in several tasks
such as speech recognition, synthesis, talking heads and language tutoring.
Most earlier inversion studies are based on point-tracking articulatory
techniques (e.g. EMA or XRMB). The advantage of rtMRI is that it provides
dynamic information about the full midsagittal plane of the upper airway, with
a high 'relative' spatial resolution. In this work, we estimated midsagittal
rtMRI images of the vocal tract for speaker dependent AAI, using MGC-LSP
spectral features as input. We applied FC-DNNs, CNNs and recurrent neural
networks, and have shown that LSTMs are the most suitable for this task. As
objective evaluation we measured normalized MSE, Structural Similarity Index
(SSIM) and its complex wavelet version (CW-SSIM). The results indicate that the
combination of FC-DNNs and LSTMs can achieve smooth generated MR images of the
vocal tract, which are similar to the original MRI recordings (average CW-SSIM:
0.94).
