Frequency & Channel Attention Network for Small Footprint Noisy Spoken Keyword Spotting
<|reference_start|>Frequency & Channel Attention Network for Small Footprint Noisy Spoken Keyword Spotting: 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.<|reference_end|>
@article{lin2024frequency,
    title={Frequency & Channel Attention Network for Small Footprint Noisy Spoken
  Keyword Spotting},
    author={Yuanxi Lin and Yuriy Evgenyevich Gapanyuk},
    journal={arXiv preprint arXiv:2407.19834},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19834},
    primaryClass={eess.AS cs.SD}
}