Silent Speech Sentence Recognition with Six-Axis Accelerometers using Conformer and CTC Algorithm
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914043465302016 |
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| author | Xie, Yudong Han, Zhifeng Xiao, Qinfan Liang, Liwei Tao, Lu-Qi Ren, Tian-Ling |
| author_facet | Xie, Yudong Han, Zhifeng Xiao, Qinfan Liang, Liwei Tao, Lu-Qi Ren, Tian-Ling |
| contents | Silent speech interfaces (SSI) are being actively developed to assist individuals with communication impairments who have long suffered from daily hardships and a reduced quality of life. However, silent sentences are difficult to segment and recognize due to elision and linking. A novel silent speech sentence recognition method is proposed to convert the facial motion signals collected by six-axis accelerometers into transcribed words and sentences. A Conformer-based neural network with the Connectionist-Temporal-Classification algorithm is used to gain contextual understanding and translate the non-acoustic signals into words sequences, solely requesting the constituent words in the database. Test results show that the proposed method achieves a 97.17% accuracy in sentence recognition, surpassing the existing silent speech recognition methods with a typical accuracy of 85%-95%, and demonstrating the potential of accelerometers as an available SSI modality for high-accuracy silent speech sentence recognition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17829 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Silent Speech Sentence Recognition with Six-Axis Accelerometers using Conformer and CTC Algorithm Xie, Yudong Han, Zhifeng Xiao, Qinfan Liang, Liwei Tao, Lu-Qi Ren, Tian-Ling Human-Computer Interaction Audio and Speech Processing Silent speech interfaces (SSI) are being actively developed to assist individuals with communication impairments who have long suffered from daily hardships and a reduced quality of life. However, silent sentences are difficult to segment and recognize due to elision and linking. A novel silent speech sentence recognition method is proposed to convert the facial motion signals collected by six-axis accelerometers into transcribed words and sentences. A Conformer-based neural network with the Connectionist-Temporal-Classification algorithm is used to gain contextual understanding and translate the non-acoustic signals into words sequences, solely requesting the constituent words in the database. Test results show that the proposed method achieves a 97.17% accuracy in sentence recognition, surpassing the existing silent speech recognition methods with a typical accuracy of 85%-95%, and demonstrating the potential of accelerometers as an available SSI modality for high-accuracy silent speech sentence recognition. |
| title | Silent Speech Sentence Recognition with Six-Axis Accelerometers using Conformer and CTC Algorithm |
| topic | Human-Computer Interaction Audio and Speech Processing |
| url | https://arxiv.org/abs/2502.17829 |