# Real-Time MRI Video synthesis from time aligned phonemes with sequence-to-sequence networks

arXiv: https://arxiv.org/abs/2210.16881 · HTML: https://mlsanity.com/paper/2210.16881/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2022.

## Built on by (most cited first, 4 of 4)

- [Speech2rtMRI: Speech-Guided Diffusion Model for Real-time MRI Video of the Vocal Tract during Speech](https://mlsanity.com/paper/2409.15525/) (2024)
- [SIREM: Speech-Informed MRI Reconstruction with Learned Sampling](https://mlsanity.com/paper/2605.18221/) (2026)
- [An Audio-textual Diffusion Model For Converting Speech Signals Into Ultrasound Tongue Imaging Data](https://mlsanity.com/paper/2403.05820/) (2024)
- [Role of the Pretraining and the Adaptation data sizes for low-resource real-time MRI video segmentation](https://mlsanity.com/paper/2502.14418/) (2025)

## Abstract (arXiv)

Real-Time Magnetic resonance imaging (rtMRI) of the midsagittal plane of the mouth is of interest for speech production research. In this work, we focus on estimating utterance level rtMRI video from the spoken phoneme sequence. We obtain time-aligned phonemes from forced alignment, to obtain frame-level phoneme sequences which are aligned with rtMRI frames. We propose a sequence-to-sequence learning model with a transformer phoneme encoder and convolutional frame decoder. We then modify the learning by using intermediary features obtained from sampling from a pretrained phoneme-conditioned variational autoencoder (CVAE). We train on 8 subjects in a subject-specific manner and demonstrate the performance with a subjective test. We also use an auxiliary task of air tissue boundary (ATB) segmentation to obtain the objective scores on the proposed models. We show that the proposed method is able to generate realistic rtMRI video for unseen utterances, and adding CVAE is beneficial for learning the sequence-to-sequence mapping for subjects where the mapping is hard to learn.