# emg2speech: Synthesizing speech from electromyography using self-supervised speech models

arXiv: https://arxiv.org/abs/2510.23969 · HTML: https://mlsanity.com/paper/2510.23969/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 17 of 17)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [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)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis](https://mlsanity.com/paper/2005.08209/) (2020)
- [Lip to Speech Synthesis with Visual Context Attentional GAN](https://mlsanity.com/paper/2204.01726/) (2022)
- [Evidence of Vocal Tract Articulation in Self-Supervised Learning of Speech](https://mlsanity.com/paper/2210.11723/) (2022)
- [Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions](https://mlsanity.com/paper/1904.02619/) (2019)
- [Digital Voicing of Silent Speech](https://mlsanity.com/paper/2010.02960/) (2020)
- [emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography](https://mlsanity.com/paper/2410.20081/) (2024)
- [Self-Supervised Models of Speech Infer Universal Articulatory Kinematics](https://mlsanity.com/paper/2310.10788/) (2023)
- [An Improved Model for Voicing Silent Speech](https://mlsanity.com/paper/2106.01933/) (2021)
- [A Cross-Modal Approach to Silent Speech with LLM-Enhanced Recognition](https://mlsanity.com/paper/2403.05583/) (2024)
- [Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication](https://mlsanity.com/paper/2311.03611/) (2023)

## Built on by (most cited first, 3 of 3)

- [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)
- [Myovox: Reading Speech from the Muscles of the Face](https://mlsanity.com/paper/2609.17548/) (2026)
- [MyoCodec: A Streaming Neural Codec for Electromyography](https://mlsanity.com/paper/2609.36687/) (2026)

## Abstract (arXiv)

We present a neuromuscular speech interface that translates electromyographic (EMG) signals recorded from orofacial muscles during speech articulation directly into audio. We find that self-supervised speech (S3) representations are strongly linearly related to the electrical power of muscle activity: a simple linear mapping predicts EMG power from S3 representations with a correlation of r = 0.85. In addition, EMG power vectors associated with distinct articulatory gestures form structured, separable clusters. Together, these observations suggest that S3 models implicitly encode articulatory mechanisms, as reflected in EMG activity. Leveraging this structure, we map EMG signals into the S3 representation space and synthesize speech, enabling end-to-end EMG-to-speech generation without explicit articulatory modeling or vocoder training. We demonstrate this system with a participant with amyotrophic lateral sclerosis (ALS), converting orofacial EMG recorded while she silently articulated speech into audio.