# Anatomy-aware cross-speaker adaptation of complete vocal-tract acoustic-to-articulatory inversion

arXiv: https://arxiv.org/abs/2609.29766 · HTML: https://mlsanity.com/paper/2609.29766/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [Self-Supervised Models of Speech Infer Universal Articulatory Kinematics](https://mlsanity.com/paper/2310.10788/) (2023)
- [Speaker-Independent Acoustic-to-Articulatory Speech Inversion](https://mlsanity.com/paper/2302.06774/) (2023)
- [Reconstruction of the Complete Vocal Tract Contour Through Acoustic to Articulatory Inversion Using Real-Time MRI Data](https://mlsanity.com/paper/2510.00914/) (2025)
- [Speaker dependent acoustic-to-articulatory inversion using real-time MRI of the vocal tract](https://mlsanity.com/paper/2008.02098/) (2020)
- [Acoustic-to-articulatory Inversion of the Complete Vocal Tract from RT-MRI with Various Audio Embeddings and Dataset Sizes](https://mlsanity.com/paper/2603.28723/) (2026)

## Abstract (arXiv)

Cross-speaker acoustic-to-articulatory inversion requires accounting for anatomical differences between speakers. We propose a geometric adaptation framework that uses anatomical landmarks, primarily on vertebrae and dental structures,to transfer predictions from a fixed inversion model to unseen speakers. An affine transformation followed by thin-plate spline (TPS) deformation maps the predicted contours of 10 vocal-tract structures into each target speaker's geometry without retraining. Landmarks are identified in one selected /u/ frame per speaker as a common phonetic reference without assuming identical articulatory configurations across speakers, and the resulting mapping is reused across recordings. We train the model on a single-speaker rt-MRI database and evaluate adaptation on eight speakers from a separate multi-speaker rt-MRI database. We compare affine and TPS configurations using 12 or 14 landmarks. Affine12+TPS14 achieves the lowest mean point-to-closest-point error of 3.19mm. These results support the combined value of anatomical landmark information and nonrigid alignment.