  Several approaches exist for the recording of articulatory movements, such as
eletromagnetic and permanent magnetic articulagraphy, ultrasound tongue imaging
and surface electromyography. Although magnetic resonance imaging (MRI) is more
costly than the above approaches, the recent developments in this area now
allow the recording of real-time MRI videos of the articulators with an
acceptable resolution. Here, we experiment with the reconstruction of the
speech signal from a real-time MRI recording using deep neural networks.
Instead of estimating speech directly, our networks are trained to output a
spectral vector, from which we reconstruct the speech signal using the WaveGlow
neural vocoder. We compare the performance of three deep neural architectures
for the estimation task, combining convolutional (CNN) and recurrence-based
(LSTM) neural layers. Besides the mean absolute error (MAE) of our networks, we
also evaluate our models by comparing the speech signals obtained using several
objective speech quality metrics like the mean cepstral distortion (MCD),
Short-Time Objective Intelligibility (STOI), Perceptual Evaluation of Speech
Quality (PESQ) and Signal-to-Distortion Ratio (SDR). The results indicate that
our approach can successfully reconstruct the gross spectral shape, but more
improvements are needed to reproduce the fine spectral details.
