  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.
