# Deep Speech Synthesis from MRI-Based Articulatory Representations

arXiv: https://arxiv.org/abs/2307.02471 · HTML: https://mlsanity.com/paper/2307.02471/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2023.

## Built on by (most cited first, 12 of 12)

- [YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection](https://mlsanity.com/paper/2408.15297/) (2024)
- [SSDM: Scalable Speech Dysfluency Modeling](https://mlsanity.com/paper/2408.16221/) (2024)
- [Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection](https://mlsanity.com/paper/2409.09621/) (2024)
- [Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency Detection](https://mlsanity.com/paper/2505.22029/) (2025)
- [Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detection](https://mlsanity.com/paper/2409.13582/) (2024)
- [Dysfluent WFST: A Framework for Zero-Shot Speech Dysfluency Transcription and Detection](https://mlsanity.com/paper/2505.16351/) (2025)
- [Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis](https://mlsanity.com/paper/2506.12073/) (2025)
- [Multimodal Segmentation for Vocal Tract Modeling](https://mlsanity.com/paper/2406.15754/) (2024)
- [MRI2Speech: Speech Synthesis from Articulatory Movements Recorded by Real-time MRI](https://mlsanity.com/paper/2412.18836/) (2024)
- [CiwaGAN: Articulatory information exchange](https://mlsanity.com/paper/2309.07861/) (2023)
- [K-Function: Joint Pronunciation Transcription and Feedback for Evaluating Kids Language Function](https://mlsanity.com/paper/2507.03043/) (2025)
- [Arti-JEPA: Adapting Video World Model to Real-Time MRI of the Vocal Tract for Speech-Production Analysis](https://mlsanity.com/paper/2609.09757/) (2026)

## Abstract (arXiv)

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.