PhonoBridge: Error-Aware Phoneme-Mediated EMG-to-Text Reconstruction
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...
| Authors | Wen Qiu, Jiacheng Zhang, Xuanda Chen et al. |
|---|---|
| Primary category | cs.SD |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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