Articulatory Feature Prediction from Surface EMG during Speech Production

2025/05/20 by Lee, Jihwan, Huang, Kevin, Avramidis, Kleanthis +6 · 2 citations
#Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Sound (cs.SD) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2505.13814

Abstract

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.

Cited by

• ARTI-6: Towards Six-dimensional Articulatory Speech Encoding
• Interpretable Modeling of Articulatory Temporal Dynamics from real-time MRI for Phoneme Recognition

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