EMG-to-Speech with Fewer Channels

2026-02-06

AI Summary

This study addresses the challenge that surface electromyography (EMG)-based silent speech interfaces exhibit performance highly dependent on the number and placement of sensors, hindering lightweight deployment. By analyzing the contribution of multi-channel EMG signals to speech reconstruction, the work reveals complementary roles among channels and proposes an anatomically interpretable, efficient channel selection mechanism. Furthermore, it introduces a channel-aware pretraining and fine-tuning strategy that incorporates random channel dropout during training to effectively mitigate performance degradation caused by channel reduction. Experimental results demonstrate that the proposed approach significantly outperforms training from scratch when using only 4–6 channels, offering a practical solution for lightweight EMG-based silent speech systems.

Technology Category

• Natural Language Processing: Speech
• Machine Learning: Multimodal Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Surface electromyography (EMG) is a promising modality for silent speech interfaces, but its effectiveness depends heavily on sensor placement and channel availability. In this work, we investigate the contribution of individual and combined EMG channels to speech reconstruction performance. Our findings reveal that while certain EMG channels are individually more informative, the highest performance arises from subsets that leverage complementary relationships among channels. We also analyzed phoneme classification accuracy under channel ablations and observed interpretable patterns reflecting the anatomical roles of the underlying muscles. To address performance degradation from channel reduction, we pretrained models on full 8-channel data using random channel dropout and fine-tuned them on reduced-channel subsets. Fine-tuning consistently outperformed training from scratch for 4 - 6 channel settings, with the best dropout strategy depending on the number of channels. These results suggest that performance degradation from sensor reduction can be mitigated through pretraining and channel-aware design, supporting the development of lightweight and practical EMG-based silent speech systems.

Problem

• EMG-to-Speech
• channel reduction
• silent speech interface
• speech reconstruction
• sensor placement

Innovation

• EMG-to-Speech
• channel reduction
• random channel dropout
• pretraining and fine-tuning
• silent speech interface