  In this paper, we study articulatory synthesis, a speech synthesis method
using human vocal tract information that offers a way to develop efficient,
generalizable and interpretable synthesizers. While recent advances have
enabled intelligible articulatory synthesis using electromagnetic
articulography (EMA), these methods lack critical articulatory information like
excitation and nasality, limiting generalization capabilities. To bridge this
gap, we propose an alternative MRI-based feature set that covers a much more
extensive articulatory space than EMA. We also introduce normalization and
denoising procedures to enhance the generalizability of deep learning methods
trained on MRI data. Moreover, we propose an MRI-to-speech model that improves
both computational efficiency and speech fidelity. Finally, through a series of
ablations, we show that the proposed MRI representation is more comprehensive
than EMA and identify the most suitable MRI feature subset for articulatory
synthesis.
