The keyword spotting system is an important part of the intelligent voice interaction system. We explore the application of convolution neural networks and depthwise separable convolution neural networks to the keyword spotting task

using the Google Speech Commands Dataset as our benchmark. We will make comparison of recognition rate

calculation amount

and storage consumption for two convolutional neural network models and propose a network model with low resource and high recognition rate for restricted devices. The experimental results show that both the traditional convolutional neural networks and the deep separable convolutional neural networks perform better than the traditional Hidden Markov model and deep learning model based on fully connected neural networks in the keyword spotting task

while the depthwise separable convolutional neural networks is more superior to the convolutional neural networks.