# Speaker-Independent Acoustic-to-Articulatory Speech Inversion

arXiv: https://arxiv.org/abs/2302.06774 · HTML: https://mlsanity.com/paper/2302.06774/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2023.

## Built on by (most cited first, 15 of 15)

- [Coding Speech through Vocal Tract Kinematics](https://mlsanity.com/paper/2406.12998/) (2024)
- [Self-Supervised Models of Speech Infer Universal Articulatory Kinematics](https://mlsanity.com/paper/2310.10788/) (2023)
- [YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection](https://mlsanity.com/paper/2408.15297/) (2024)
- [Towards Hierarchical Spoken Language Dysfluency Modeling](https://mlsanity.com/paper/2401.10015/) (2024)
- [Articulatory Feature Prediction from Surface EMG during Speech Production](https://mlsanity.com/paper/2505.13814/) (2025)
- [RT-VC: Real-Time Zero-Shot Voice Conversion with Speech Articulatory Coding](https://mlsanity.com/paper/2506.10289/) (2025)
- [Towards an Interpretable Representation of Speaker Identity via Perceptual Voice Qualities](https://mlsanity.com/paper/2310.02497/) (2023)
- [Can we reconstruct a dysarthric voice with the large speech model Parler TTS?](https://mlsanity.com/paper/2506.04397/) (2025)
- [On the Relationship between Accent Strength and Articulatory Features](https://mlsanity.com/paper/2507.03149/) (2025)
- [ARTI-6: Towards Six-dimensional Articulatory Speech Encoding](https://mlsanity.com/paper/2509.21447/) (2025)
- [UNFUSED: UNsupervised Finetuning Using SElf supervised Distillation](https://mlsanity.com/paper/2303.05668/) (2023)
- [Speaker- and Text-Independent Estimation of Articulatory Movements and Phoneme Alignments from Speech](https://mlsanity.com/paper/2407.03132/) (2024)
- [Speech Emotion Recognition with Phonation Excitation Information and Articulatory Kinematics](https://mlsanity.com/paper/2511.07955/) (2025)
- [ArtBoost: Synthetic Articulatory Data Augmentation for Acoustic-to-Articulatory Inversion](https://mlsanity.com/paper/2606.16327/) (2026)
- [Anatomy-aware cross-speaker adaptation of complete vocal-tract acoustic-to-articulatory inversion](https://mlsanity.com/paper/2609.29766/) (2026)

## Abstract (arXiv)

To build speech processing methods that can handle speech as naturally as humans, researchers have explored multiple ways of building an invertible mapping from speech to an interpretable space. The articulatory space is a promising inversion target, since this space captures the mechanics of speech production. To this end, we build an acoustic-to-articulatory inversion (AAI) model that leverages self-supervision to generalize to unseen speakers. Our approach obtains 0.784 correlation on an electromagnetic articulography (EMA) dataset, improving the state-of-the-art by 12.5%. Additionally, we show the interpretability of these representations through directly comparing the behavior of estimated representations with speech production behavior. Finally, we propose a resynthesis-based AAI evaluation metric that does not rely on articulatory labels, demonstrating its efficacy with an 18-speaker dataset.