Advances in EMG-to-Speech Generation

Updated 29 October 2025

EMG-to-speech generation is the process of converting muscle signals into audible speech using advanced deep learning and feature extraction methods.
Modern systems leverage multi-channel electrode arrays, including wearable designs, to capture articulatory movements with high classification accuracy.
State-of-the-art architectures employ direct regression, intermediate articulatory mapping, and diffusion models to improve intelligibility and naturalness.

Electromyography-to-speech (EMG-to-speech) generation is the process of converting surface electromyographic signals—recorded from muscles involved in speech articulation—into intelligible audible speech. This non-acoustic, neuroprosthetic approach targets users unable to vocalize, such as individuals with laryngectomy, neuromuscular disease, or trauma-induced speech loss. Modern systems leverage deep learning to map EMG to speech representations, increasingly exploiting articulatory, acoustic, and self-supervised latent features, with performance now competitive with early automatic speech recognition (ASR) benchmarks.

1. Signal Acquisition and Device Form Factors

Surface electromyography (sEMG) for speech neuroprosthetics typically employs noninvasive, multi-channel electrode arrays placed on the face (perioral, zygomatic, mentalis, submentalis) and/or neck to capture muscle activity during speech articulation (Gonzalez-Lopez et al., 2020, Gowda et al., 28 Oct 2025, Wu et al., 31 Jul 2024). Device innovations include:

Traditional arrays: 8–31 monopolar electrodes on lips, jaw, chin, and cheeks, referenced to earlobes or mastoid (Gowda et al., 28 Oct 2025).
Neckband designs: Ten dry gold-plated electrodes evenly distributed around the neck yield 92.7% word classification accuracy—comparable to face-based arrays (Wu et al., 31 Jul 2024). Accuracy increases and phonological coverage improves with larger electrode counts.
Wearables: Integration of textile-based electrodes into headphone earmuffs enables discreet, comfortable, and robust sEMG acquisition for silent-speech interfaces (Tang et al., 11 Apr 2025).
Wireless systems: Wi-Fi transmission and battery power support untethered, daily use. Low-latency pipelines are feasible due to rapid sEMG acquisition (EMG typically leads articulator motion by ~60 ms) (Gonzalez-Lopez et al., 2020).

2. Feature Extraction and EMG Representation

Standard sEMG pipelines involve signal preprocessing (amplification, bandpass filtering in 20–1000 Hz), frame segmentation (e.g., 20–27 ms windows, 10 ms stride), and feature extraction:

Time-domain and statistical features: Energy, zero-crossing rate, kurtosis, and higher moments (Gaddy et al., 2020, Wu et al., 31 Jul 2024).
Frequency-domain features: STFT-derived magnitudes, binned spectrograms (Gowda et al., 28 Oct 2025, Wu et al., 31 Jul 2024).
Covariance-based features: Channel-wise EMG power and cross-electrode covariance are strongly linearly related to self-supervised speech features (Pearson’s $r\approx$ 0.85), outperforming standard spectral representations (Gowda et al., 28 Oct 2025).
Session embeddings: Adaptations for session/electrode placement variability are critical, typically handled by trainable bias vectors per session (Gaddy et al., 2020, Gaddy et al., 2021).

EMG arrays closer to target articulators (lips, jaw) yield higher prediction reliability for those movements, as established by leave-one-out ablations (Lee et al., 20 May 2025), while internal articulators (e.g., tongue) are less accessible from the skin surface.

3. Learning Architectures and Mapping Paradigms

Several deep learning paradigms underpin EMG-to-speech systems:

Direct regression (EMG-to-acoustic): Feedforward, convolutional, or recurrent models (LSTM, Transformer), mapping EMG features to Mel-frequency cepstral coefficients (MFCCs), log Mel spectrograms, or speech units (Gaddy et al., 2021, Gaddy et al., 2020, Ren et al., 11 May 2024, Gowda et al., 28 Oct 2025).
Articulatory intermediate mapping: Predict electromagnetic articulography (EMA) sensor trajectories, pitch, and loudness from sEMG, then synthesize speech with external articulatory-to-acoustic models. This approach achieves high articulatory correlation ( $\approx$ 0.9) and competitive intelligibility (WER 15.5%) (Lee et al., 20 May 2025).
Self-supervised representations: EMG signals are mapped directly to discrete or continuous features from pretrained models such as HuBERT or WavLM, then used with frozen speech synthesizers. This supports large-vocabulary EMG-to-audio with no explicit articulatory model and requires only a CTC loss (Gowda et al., 28 Oct 2025, Wu et al., 31 Jul 2024).
Diffusion modeling: To counteract over-smoothing in regression models and improve naturalness, score-based diffusion models refine EMG-predicted Mel spectrograms before vocoder synthesis, significantly raising perceptual MOS by up to 0.5 points (Ren et al., 11 May 2024).

Table 1: Comparison of EMG-to-Speech Mapping Approaches

Approach	Intermediate Target	Key Metric (WER/PER)	Notable Feature
Direct regression	MFCC/log-Mel	WER: 42.2% (Gaddy et al., 2021)	End-to-end, LSTM/Transformer
Articulatory intermediate	EMA/Pitch/Loudness	WER: 15.5% (Lee et al., 20 May 2025)	Parallel predictors, interpretable articulation
Self-supervised units	HuBERT/WavLM Discrete	PER: 41.4% (Gowda et al., 28 Oct 2025)	Linear EMG-unit relationship
Diffusion-based refinement	Mel Spectrogram	MOS: 3.70 (Ren et al., 11 May 2024)	Score-based generative modeling

4. Data Alignment, Target Transfer, and Self-Supervision

Data scarcity and misalignment are major challenges:

Target transfer (TT) for silent EMG: To enable learning from silent speech (where no audio is available), audio features from vocalized EMG are temporally transferred to corresponding silent segments via dynamic time warping (DTW) and canonical correlation analysis (CCA) (Gaddy et al., 2020). Iterative alignment with predicted features further boosts performance.
Phoneme-level confidence self-training: Synthetic EMG generated from large audio corpora (LibriSpeech) enables semi-supervised EMG-to-speech modeling; filtering by phoneme-level confidence loss yields superior generalization and state-of-the-art WER (18.03%) (Chen et al., 13 Jun 2025).
Cross-modal contrastive learning: Losses such as crossCon and supTcon align EMG and audio representations in a shared latent space, permitting pretraining with audio-only data and reducing EMG data requirements (Benster et al., 2 Mar 2024).

5. Evaluation, Performance Metrics, and Robustness

Multiple metrics assess EMG-to-speech systems, including:

Word Error Rate (WER): ASR-based intelligibility (e.g., 3.6% for closed-vocab, 68% for open-vocab in early systems, 12.2% SOTA on silent speech (Gaddy et al., 2020, Benster et al., 2 Mar 2024)).
Phoneme Error Rate (PER), SpeechBERTScore (SBS), and MOS: For speech quality and perceptual similarity (Lee et al., 20 May 2025, Ren et al., 11 May 2024).
Mean Opinion Score (MOS): Diff-ETS raises MOS from 3.19 (baseline) to 3.70 (Ren et al., 11 May 2024).
Alignment-free benchmarks: Covariance-based EMG features enable high-performant, alignment-free sequence-to-sequence learning and clustering (Gowda et al., 28 Oct 2025).

Robustness must account for session/electrode variability, motion artifacts, speaker adaptation, and the difference between silent and vocalized articulations. Adaptive attention (e.g., Squeeze-and-Excitation blocks), session embeddings, and confidence filtering mitigate these factors (Tang et al., 11 Apr 2025, Gaddy et al., 2021, Chen et al., 13 Jun 2025).

6. Device Optimization, Electrode Placement, and Wearability

EMG-to-speech system design increasingly balances performance with wearability and practical deployment:

Electrode optimization: Knowledge-driven selection identifies key site–articulator pairings (e.g., chin for tongue, cheek for lips), enabling high performance with only 2–4 electrodes (Lee et al., 20 May 2025, Wu et al., 31 Jul 2024).
Textile and neckband devices: Patch-free, dry-electrode arrays in headphones or neckbands support daily, discreet use, with accuracy matching adhesive arrays (Tang et al., 11 Apr 2025, Wu et al., 31 Jul 2024).
Adaptive models: Robustness to signal loss and motion is achieved through adaptive channel attention and explainable feature weighting (Tang et al., 11 Apr 2025).

7. Challenges, Limitations, and Future Directions

Persistent challenges include session and device variability, speaker dependence, tongue articulation coverage, and data scarcity for impaired users (Gonzalez-Lopez et al., 2020). Recent advances—such as self-supervised latent mapping (Gowda et al., 28 Oct 2025), cross-modal contrastive training (Benster et al., 2 Mar 2024), and semi-supervised data augmentation (Chen et al., 13 Jun 2025)—address data limitations and offer speaker-independent modeling. Clinical translation awaits further validation on impaired populations, improvement in noninvasive sensor comfort, and robust open-vocabulary synthesis at scale.

Conclusion

EMG-to-speech generation is converging toward practical, open-vocabulary, and speaker-independent neuroprosthetic speech through an overview of advanced signal processing, deep learning, articulatory modeling, and wearable sensor design. Foundational discoveries—such as strong linear relationships between EMG power and self-supervised speech features, and robust performance with minimal electrodes—point toward near-future daily-use silent speech interfaces and speech restoration systems with naturalistic output and broad accessibility (Gowda et al., 28 Oct 2025, Wu et al., 31 Jul 2024, Lee et al., 20 May 2025).