  Robustness against noise is critical for keyword spotting (KWS) in real-world
environments. To improve the robustness, a speech enhancement front-end is
involved. Instead of treating the speech enhancement as a separated
preprocessing before the KWS system, in this study, a pre-trained speech
enhancement front-end and a convolutional neural networks (CNNs) based KWS
system are concatenated, where a feature transformation block is used to
transform the output from the enhancement front-end into the KWS system's
input. The whole model is trained jointly, thus the linguistic and other useful
information from the KWS system can be back-propagated to the enhancement
front-end to improve its performance. To fit the small-footprint device, a
novel convolution recurrent network is proposed, which needs fewer parameters
and computation and does not degrade performance. Furthermore, by changing the
input features from the power spectrogram to Mel-spectrogram, less computation
and better performance are obtained. our experimental results demonstrate that
the proposed method significantly improves the KWS system with respect to noise
robustness.
