  In this paper, we propose an attention-based end-to-end model for
multi-channel keyword spotting (KWS), which is trained to optimize the KWS
result directly. As a result, our model outperforms the baseline model with
signal pre-processing techniques in both the clean and noisy testing data. We
also found that multi-task learning results in a better performance when the
training and testing data are similar. Transfer learning and multi-target
spectral mapping can dramatically enhance the robustness to the noisy
environment. At 0.1 false alarm (FA) per hour, the model with transfer learning
and multi-target mapping gain an absolute 30% improvement in the wake-up rate
in the noisy data with SNR about -20.
