# Geometry of orofacial neuromuscular signals: speech articulation decoding using surface electromyography

arXiv: https://arxiv.org/abs/2411.02591 · HTML: https://mlsanity.com/paper/2411.02591/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 11 of 11)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [A Riemannian Network for SPD Matrix Learning](https://mlsanity.com/paper/1608.04233/) (2016)
- [Neural Manifold Ordinary Differential Equations](https://mlsanity.com/paper/2006.10254/) (2020)
- [Digital Voicing of Silent Speech](https://mlsanity.com/paper/2010.02960/) (2020)
- [An Improved Model for Voicing Silent Speech](https://mlsanity.com/paper/2106.01933/) (2021)
- [Deep Efficient Continuous Manifold Learning for Time Series Modeling](https://mlsanity.com/paper/2112.03379/) (2021)
- [Topology of surface electromyogram signals: hand gesture decoding on Riemannian manifolds](https://mlsanity.com/paper/2311.08548/) (2023)

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

- [Myovox: Reading Speech from the Muscles of the Face](https://mlsanity.com/paper/2609.17548/) (2026)

## Abstract (arXiv)

Objective. In this article, we present data and methods for decoding speech articulations using surface electromyogram (EMG) signals. EMG-based speech neuroprostheses offer a promising approach for restoring audible speech in individuals who have lost the ability to speak intelligibly due to laryngectomy, neuromuscular diseases, stroke, or trauma-induced damage (e.g., from radiotherapy) to the speech articulators. Approach. To achieve this, we collect EMG signals from the face, jaw, and neck as subjects articulate speech, and we perform EMG-to-speech translation. Main results. Our findings reveal that the manifold of symmetric positive definite (SPD) matrices serves as a natural embedding space for EMG signals. Specifically, we provide an algebraic interpretation of the manifold-valued EMG data using linear transformations, and we analyze and quantify distribution shifts in EMG signals across individuals. Significance. Overall, our approach demonstrates significant potential for developing neural networks that are both data- and parameter-efficient, an important consideration for EMG-based systems, which face challenges in large-scale data collection and operate under limited computational resources on embedded devices.