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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.21345 |
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| _version_ | 1866929444233412608 |
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| author | Wu, Peter Kaveh, Ryan Nautiyal, Raghav Zhang, Christine Guo, Albert Kachinthaya, Anvitha Mishra, Tavish Yu, Bohan Black, Alan W Muller, Rikky Anumanchipalli, Gopala Krishna |
| author_facet | Wu, Peter Kaveh, Ryan Nautiyal, Raghav Zhang, Christine Guo, Albert Kachinthaya, Anvitha Mishra, Tavish Yu, Bohan Black, Alan W Muller, Rikky Anumanchipalli, Gopala Krishna |
| contents | Electrodes for decoding speech from electromyography (EMG) are typically placed on the face, requiring adhesives that are inconvenient and skin-irritating if used regularly. We explore a different device form factor, where dry electrodes are placed around the neck instead. 11-word, multi-speaker voiced EMG classifiers trained on data recorded with this device achieve 92.7% accuracy. Ablation studies reveal the importance of having more than two electrodes on the neck, and phonological analyses reveal similar classification confusions between neck-only and neck-and-face form factors. Finally, speech-EMG correlation experiments demonstrate a linear relationship between many EMG spectrogram frequency bins and self-supervised speech representation dimensions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_21345 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards EMG-to-Speech with a Necklace Form Factor Wu, Peter Kaveh, Ryan Nautiyal, Raghav Zhang, Christine Guo, Albert Kachinthaya, Anvitha Mishra, Tavish Yu, Bohan Black, Alan W Muller, Rikky Anumanchipalli, Gopala Krishna Audio and Speech Processing Electrodes for decoding speech from electromyography (EMG) are typically placed on the face, requiring adhesives that are inconvenient and skin-irritating if used regularly. We explore a different device form factor, where dry electrodes are placed around the neck instead. 11-word, multi-speaker voiced EMG classifiers trained on data recorded with this device achieve 92.7% accuracy. Ablation studies reveal the importance of having more than two electrodes on the neck, and phonological analyses reveal similar classification confusions between neck-only and neck-and-face form factors. Finally, speech-EMG correlation experiments demonstrate a linear relationship between many EMG spectrogram frequency bins and self-supervised speech representation dimensions. |
| title | Towards EMG-to-Speech with a Necklace Form Factor |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2407.21345 |