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arXiv · 2610.27678

PhonoBridge: Error-Aware Phoneme-Mediated EMG-to-Text Reconstruction

Wen Qiu·Jiacheng Zhang·Xuanda Chen·Siqi Cai·Haizhou Li
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Abstract

Electromyography (EMG) provides a non-invasive means of decoding silently articulated speech into text, but accurate decoding remains challenging due to limited availability of paired EMG-text data. We introduce PhonoBridge, a two-stage framework comprising a channel-aware EMG-to-phoneme frontend and a large language model (LLM) backend for text reconstruction. The frontend is trained on paired EMG-text data that consists of channel-wise feature extraction, cross-channel fusion, and electrode embeddings. With phonemes as the intermediate representation, we expand the backend's training data by converting LibriSpeech transcripts into phoneme-text pairs without additional EMG recordings. To adapt the LLM to frontend noise, we introduce Error-Profile-Guided Augmentation (EPGA), which perturbs transcript-derived phoneme sequences according to error patterns estimated from frontend predictions, generating additional training pairs for the LLM. On the Open Vocabulary silent-speech dataset, PhonoBridge achieves 24.77% WER, outperforming the previously reported best of 28.27% on the same test split.

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Wen Qiu, Jiacheng Zhang, Xuanda Chen, Siqi Cai, Haizhou Li. 2026-10-20. PhonoBridge: Error-Aware Phoneme-Mediated EMG-to-Text Reconstruction. https://arxiv.org/abs/2610.27678

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