Title
A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting.
Abstract
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.
Year
Venue
DocType
2019
CoRR
Journal
Volume
Citations 
PageRank 
abs/1906.08415
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Yue Gu103.04
Zhihao Du221.75
Hui Zhang314544.24
Xueliang Zhang48019.41