IR-UWB Radar-Based Contactless Silent Speech Recognition with Attention-Enhanced Temporal Convolutional Networks
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916980348420096 |
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| author | Lee, Sunghwa Yu, Jaewon |
| author_facet | Lee, Sunghwa Yu, Jaewon |
| contents | Silent speech recognition (SSR) is a technology that recognizes speech content from non-acoustic speech-related biosignals. This paper utilizes an attention-enhanced temporal convolutional network architecture for contactless IR-UWB radar-based SSR, leveraging deep learning to learn discriminative representations directly from minimally processed radar signals. The architecture integrates temporal convolutions with self-attention and squeeze-and-excitation mechanisms to capture articulatory patterns. Evaluated on a 50-word recognition task using leave-one-session-out cross-validation, our approach achieves an average test accuracy of 91.1\% compared to 74.0\% for the conventional hand-crafted feature method, demonstrating significant improvement through end-to-end learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26409 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IR-UWB Radar-Based Contactless Silent Speech Recognition with Attention-Enhanced Temporal Convolutional Networks Lee, Sunghwa Yu, Jaewon Audio and Speech Processing Silent speech recognition (SSR) is a technology that recognizes speech content from non-acoustic speech-related biosignals. This paper utilizes an attention-enhanced temporal convolutional network architecture for contactless IR-UWB radar-based SSR, leveraging deep learning to learn discriminative representations directly from minimally processed radar signals. The architecture integrates temporal convolutions with self-attention and squeeze-and-excitation mechanisms to capture articulatory patterns. Evaluated on a 50-word recognition task using leave-one-session-out cross-validation, our approach achieves an average test accuracy of 91.1\% compared to 74.0\% for the conventional hand-crafted feature method, demonstrating significant improvement through end-to-end learning. |
| title | IR-UWB Radar-Based Contactless Silent Speech Recognition with Attention-Enhanced Temporal Convolutional Networks |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.26409 |