  Building efficient architecture in neural speech processing is paramount to
success in keyword spotting deployment. However, it is very challenging for
lightweight models to achieve noise robustness with concise neural operations.
In a real-world application, the user environment is typically noisy and may
also contain reverberations. We proposed a novel feature interactive
convolutional model with merely 100K parameters to tackle this under the noisy
far-field condition. The interactive unit is proposed in place of the attention
module that promotes the flow of information with more efficient computations.
Moreover, curriculum-based multi-condition training is adopted to attain better
noise robustness. Our model achieves 98.2% top-1 accuracy on Google Speech
Command V2-12 and is competitive against large transformer models under the
designed noise condition.
