mlsanityData to 14 Oct 2026

Articulatory Feature Prediction from Surface EMG during Speech Production

arXiv 2505.13814 ↗First posted 20 May 2025eess.AS cs.AI cs.SDcited by 4

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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.

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

20 of 32 references.

References · 20

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  4. 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,154
  5. 2022Decoding speech perception from non-invasive brain recordingscited by 130
  6. 2021Simple and Effective Zero-shot Cross-lingual Phoneme Recognitioncited by 79
  7. 2024SpeechBERTScore: Reference-Aware Automatic Evaluation of Speech Generation Leveraging NLP Evaluation Metricscited by 38
  8. 2023Towards Voice Reconstruction from EEG during Imagined Speechcited by 26
  9. 2020Digital Voicing of Silent Speechcited by 24
  10. 2024Coding Speech through Vocal Tract Kinematicscited by 18
  11. 2021An Improved Model for Voicing Silent Speechcited by 15
  12. 2023Speaker-Independent Acoustic-to-Articulatory Speech Inversioncited by 15
8 more
  1. 2020Silent Speech Interfaces for Speech Restoration: A Reviewcited by 14
  2. 2022Deep Speech Synthesis from Articulatory Representationscited by 10
  3. 2024Toward Fully-End-to-End Listened Speech Decoding from EEG Signalscited by 8
  4. 2025Enhancing Listened Speech Decoding from EEG via Parallel Phoneme Sequence Predictioncited by 6
  5. 2023Neural2Speech: A Transfer Learning Framework for Neural-Driven Speech Reconstructioncited by 4
  6. 2024Diff-ETS: Learning a Diffusion Probabilistic Model for Electromyography-to-Speech Conversioncited by 4
  7. 2021Sequence-to-Sequence Voice Reconstruction for Silent Speech in a Tonal Languagecited by 1
  8. 2024Deep Speech Synthesis from Multimodal Articulatory Representationscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2026An Approach to Simultaneous Acquisition of Real-Time MRI Video, EEG, and Surface EMG for Articulatory, Brain, and Muscle Activity During Speech Productioncited by 2
  2. 2025ARTI-6: Towards Six-dimensional Articulatory Speech Encodingcited by 1
  3. 2026Affect Decoding in Phonated and Silent Speech Production from Surface EMGcited by 1
  4. 2026MyoCodec: A Streaming Neural Codec for Electromyographycited by 0