A Parallel Ultra-Low Power Silent Speech Interface based on a Wearable, Fully-dry EMG Neckband
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911177983918080 |
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| author | Meier, Fiona Spacone, Giusy Frey, Sebastian Benini, Luca Cossettini, Andrea |
| author_facet | Meier, Fiona Spacone, Giusy Frey, Sebastian Benini, Luca Cossettini, Andrea |
| contents | 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$7% for the vocalized and silent experiments, respectively. These results highlight the robustness of the proposed approach and the promise of energy-efficient silent-speech decoding. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_21964 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Parallel Ultra-Low Power Silent Speech Interface based on a Wearable, Fully-dry EMG Neckband Meier, Fiona Spacone, Giusy Frey, Sebastian Benini, Luca Cossettini, Andrea Systems and Control Sound Audio and Speech Processing Signal Processing We present a wearable, fully-dry, and ultra-low power EMG system for silent speech recognition, integrated into a textile neckband to enable comfortable, non-intrusive use. The system features 14 fully-differential EMG channels and is based on the BioGAP-Ultra platform for ultra-low power (22 mW) biosignal acquisition and wireless transmission. We evaluate its performance on eight speech commands under both vocalized and silent articulation, achieving average classification accuracies of 87$\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. |
| title | A Parallel Ultra-Low Power Silent Speech Interface based on a Wearable, Fully-dry EMG Neckband |
| topic | Systems and Control Sound Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2509.21964 |