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Robustness against noise is critical for keyword spotting (KWS) in real-worldenvironments. To improve the robustness, a speech enhancement front-end isinvolved. Instead of treating the speech enhancement as a separatedpreprocessing before the KWS system, in this study, a pre-trained speechenhancement front-end and a convolutional neural networks (CNNs) based KWSsystem are concatenated, where a feature transformation block is used totransform the output from the enhancement front-end into the KWS system'sinput. The whole model is trained jointly, thus the linguistic and other usefulinformation from the KWS system can be back-propagated to the enhancementfront-end to improve its performance. To fit the small-footprint device, anovel convolution recurrent network is proposed, which needs fewer parametersand computation and does not degrade performance. Furthermore, by changing theinput features from the power spectrogram to Mel-spectrogram, less computationand better performance are obtained. our experimental results demonstrate thatthe proposed method significantly improves the KWS system with respect to noiserobustness.

A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting