Frequency & Channel Attention Network for Small Footprint Noisy Spoken Keyword Spotting
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914891330224128 |
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| author | Lin, Yuanxi Gapanyuk, Yuriy Evgenyevich |
| author_facet | Lin, Yuanxi Gapanyuk, Yuriy Evgenyevich |
| contents | In this paper, we aim to improve the robustness of Keyword Spotting (KWS) systems in noisy environments while keeping a small memory footprint. We propose a new convolutional neural network (CNN) called FCA-Net, which combines mixer unit-based feature interaction with a two-dimensional convolution-based attention module. First, we introduce and compare lightweight attention methods to enhance noise robustness in CNN. Then, we propose an attention module that creates fine-grained attention weights to capture channel and frequency-specific information, boosting the model's ability to handle noisy conditions. By combining the mixer unit-based feature interaction with the attention module, we enhance performance. Additionally, we use a curriculum-based multi-condition training strategy. Our experiments show that our system outperforms current state-of-the-art solutions for small-footprint KWS in noisy environments, making it reliable for real-world use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19834 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Frequency & Channel Attention Network for Small Footprint Noisy Spoken Keyword Spotting Lin, Yuanxi Gapanyuk, Yuriy Evgenyevich Audio and Speech Processing Sound In this paper, we aim to improve the robustness of Keyword Spotting (KWS) systems in noisy environments while keeping a small memory footprint. We propose a new convolutional neural network (CNN) called FCA-Net, which combines mixer unit-based feature interaction with a two-dimensional convolution-based attention module. First, we introduce and compare lightweight attention methods to enhance noise robustness in CNN. Then, we propose an attention module that creates fine-grained attention weights to capture channel and frequency-specific information, boosting the model's ability to handle noisy conditions. By combining the mixer unit-based feature interaction with the attention module, we enhance performance. Additionally, we use a curriculum-based multi-condition training strategy. Our experiments show that our system outperforms current state-of-the-art solutions for small-footprint KWS in noisy environments, making it reliable for real-world use. |
| title | Frequency & Channel Attention Network for Small Footprint Noisy Spoken Keyword Spotting |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2407.19834 |