# ARTI-6: Towards Six-dimensional Articulatory Speech Encoding

arXiv: https://arxiv.org/abs/2509.21447 · HTML: https://mlsanity.com/paper/2509.21447/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 39 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022](https://mlsanity.com/paper/2204.02152/) (2022)
- [LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus](https://mlsanity.com/paper/2305.18802/) (2023)
- [Evidence of Vocal Tract Articulation in Self-Supervised Learning of Speech](https://mlsanity.com/paper/2210.11723/) (2022)
- [Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations](https://mlsanity.com/paper/2303.01664/) (2023)
- [A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images](https://mlsanity.com/paper/2102.07896/) (2021)
- [Coding Speech through Vocal Tract Kinematics](https://mlsanity.com/paper/2406.12998/) (2024)
- [Speaker-Independent Acoustic-to-Articulatory Speech Inversion](https://mlsanity.com/paper/2302.06774/) (2023)
- [Deep Speech Synthesis from Articulatory Representations](https://mlsanity.com/paper/2209.06337/) (2022)
- [Articulatory Feature Prediction from Surface EMG during Speech Production](https://mlsanity.com/paper/2505.13814/) (2025)
- [Towards disentangling the contributions of articulation and acoustics in multimodal phoneme recognition](https://mlsanity.com/paper/2505.24059/) (2025)
- [Enhancing Acoustic-to-Articulatory Speech Inversion by Incorporating Nasality](https://mlsanity.com/paper/2506.09231/) (2025)
- [On the Relationship between Accent Strength and Articulatory Features](https://mlsanity.com/paper/2507.03149/) (2025)
- [A long-form single-speaker real-time MRI speech dataset and benchmark](https://mlsanity.com/paper/2509.14479/) (2025)

## Built on by (most cited first, 1 of 1)

- [An Approach to Simultaneous Acquisition of Real-Time MRI Video, EEG, and Surface EMG for Articulatory, Brain, and Muscle Activity During Speech Production](https://mlsanity.com/paper/2603.04840/) (2026)

## Abstract (arXiv)

We propose ARTI-6, a compact six-dimensional articulatory speech encoding framework derived from real-time MRI data that captures crucial vocal tract regions including the velum, tongue root, and larynx. ARTI-6 consists of three components: (1) a six-dimensional articulatory feature set representing key regions of the vocal tract; (2) an articulatory inversion model, which predicts articulatory features from speech acoustics leveraging speech foundation models, achieving a prediction correlation of 0.87; and (3) an articulatory synthesis model, which reconstructs intelligible speech directly from articulatory features, showing that even a low-dimensional representation can generate natural-sounding speech. Together, ARTI-6 provides an interpretable, computationally efficient, and physiologically grounded framework for advancing articulatory inversion, synthesis, and broader speech technology applications. The source code and speech samples are publicly available.