可穿戴干电极肌电颈带实现低功耗无声语音识别
A Parallel Ultra-Low Power Silent Speech Interface based on a Wearable, Fully-dry EMG Neckband
- 14通道差分肌电信号采集，22mW超低功耗无线传输
- 静音发音识别准确率68%，跨会话测试达54%
- 适合长期佩戴的无声输入场景，如助听或专注工作
我们提出一种集成于纺织颈带中的可穿戴、全干式、超低功耗肌电（EMG）系统，用于无声语音识别，实现舒适无感使用。系统采用14个全差分EMG通道，基于BioGAP-Ultra平台实现22mW超低功耗生物信号采集与无线传输。在八种语音指令下评估性能，有声和静音发音的平均分类准确率分别为87±3%和68±3%（5折交叉验证）。为模拟日常使用，通过会话间重新定位颈带引入变异性，留一会话外测试分别获得64±18%和54±7%的准确率。结果表明该方法具备良好鲁棒性，验证了能效型无声语音解码的可行性。
原文摘要 · Abstract (English)
We present a wearable, fully-dry, and ultra-low power EMG system for silent speech recognition, integrated into a textile neckband to enable comfortable, non-intrusive use. The system features 14 fully-differential EMG channels and is based on the BioGAP-Ultra platform for ultra-low power (22 mW) biosignal acquisition and wireless transmission. We evaluate its performance on eight speech commands under both vocalized and silent articulation, achieving average classification accuracies of 87$\pm$3% and 68$\pm$3% respectively, with a 5-fold CV approach. To mimic everyday-life conditions, we introduce session-to-session variability by repositioning the neckband between sessions, achieving leave-one-session-out accuracies of 64$\pm$18% and 54$\pm$7% for the vocalized and silent experiments, respectively. These results highlight the robustness of the proposed approach and the promise of energy-efficient silent-speech decoding.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。