arXiv cs.CL· Wen Qiu, Jiacheng Zhang, Xuanda Chen, Siqi Cai, Haizhou Li·· 12 小时前AI 评分31
PhonoBridge：误差感知的音素中介 EMG 到文本重建框架
PhonoBridge: Error-Aware Phoneme-Mediated EMG-to-Text Reconstruction
AI 导读
PhonoBridge 是一个两阶段框架，由通道感知的 EMG 到音素前端与大语言模型后端组成，在 Open Vocabulary 静默语音数据集上实现 24.77% WER，优于此前同测试集最佳的 28.27%。
正文 · 原文
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.
| Subjects: | Sound (cs.SD); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2610.27678 [cs.SD] |
| (or arXiv:2610.27678v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2610.27678 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wen Qiu [view email]
[v1]
Tue, 20 Oct 2026 05:48:21 UTC (12,075 KB)
来源：arXiv cs.CL · arxiv.org