# Articulatory Feature Prediction from Surface EMG during Speech Production

arXiv: https://arxiv.org/abs/2505.13814 · HTML: https://mlsanity.com/paper/2505.13814/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [SpeechBERTScore: Reference-Aware Automatic Evaluation of Speech Generation Leveraging NLP Evaluation Metrics](https://mlsanity.com/paper/2401.16812/) (2024)
- [Towards Voice Reconstruction from EEG during Imagined Speech](https://mlsanity.com/paper/2301.07173/) (2023)
- [Digital Voicing of Silent Speech](https://mlsanity.com/paper/2010.02960/) (2020)
- [Coding Speech through Vocal Tract Kinematics](https://mlsanity.com/paper/2406.12998/) (2024)
- [An Improved Model for Voicing Silent Speech](https://mlsanity.com/paper/2106.01933/) (2021)
- [Speaker-Independent Acoustic-to-Articulatory Speech Inversion](https://mlsanity.com/paper/2302.06774/) (2023)
- [Silent Speech Interfaces for Speech Restoration: A Review](https://mlsanity.com/paper/2009.02110/) (2020)
- [Deep Speech Synthesis from Articulatory Representations](https://mlsanity.com/paper/2209.06337/) (2022)
- [Toward Fully-End-to-End Listened Speech Decoding from EEG Signals](https://mlsanity.com/paper/2406.08644/) (2024)
- [Enhancing Listened Speech Decoding from EEG via Parallel Phoneme Sequence Prediction](https://mlsanity.com/paper/2501.04844/) (2025)
- [Neural2Speech: A Transfer Learning Framework for Neural-Driven Speech Reconstruction](https://mlsanity.com/paper/2310.04644/) (2023)
- [Diff-ETS: Learning a Diffusion Probabilistic Model for Electromyography-to-Speech Conversion](https://mlsanity.com/paper/2405.08021/) (2024)
- [Sequence-to-Sequence Voice Reconstruction for Silent Speech in a Tonal Language](https://mlsanity.com/paper/2108.00190/) (2021)
- [Deep Speech Synthesis from Multimodal Articulatory Representations](https://mlsanity.com/paper/2412.13387/) (2024)

## Built on by (most cited first, 4 of 4)

- [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)
- [ARTI-6: Towards Six-dimensional Articulatory Speech Encoding](https://mlsanity.com/paper/2509.21447/) (2025)
- [Affect Decoding in Phonated and Silent Speech Production from Surface EMG](https://mlsanity.com/paper/2603.11715/) (2026)
- [MyoCodec: A Streaming Neural Codec for Electromyography](https://mlsanity.com/paper/2609.36687/) (2026)

## Abstract (arXiv)

We present a model for predicting articulatory features from surface electromyography (EMG) signals during speech production. The proposed model integrates convolutional layers and a Transformer block, followed by separate predictors for articulatory features. Our approach achieves a high prediction correlation of approximately 0.9 for most articulatory features. Furthermore, we demonstrate that these predicted articulatory features can be decoded into intelligible speech waveforms. To our knowledge, this is the first method to decode speech waveforms from surface EMG via articulatory features, offering a novel approach to EMG-based speech synthesis. Additionally, we analyze the relationship between EMG electrode placement and articulatory feature predictability, providing knowledge-driven insights for optimizing EMG electrode configurations. The source code and decoded speech samples are publicly available.