  Large size models are implemented in recently ASR system to deal with complex
speech recognition problems. The num- ber of parameters in these models makes
them hard to deploy, especially on some resource-short devices such as car
tablet. Besides this, at most of time, ASR system is used to deal with
real-time problem such as keyword spotting (KWS). It is contradictory to the
fact that large model requires long com- putation time. To deal with this
problem, we apply some sparse algo- rithms to reduces number of parameters in
some widely used models, Deep Neural Network (DNN) KWS, which requires real
short computation time. We can prune more than 90 % even 95% of parameters in
the model with tiny effect decline. And the sparse model performs better than
baseline models which has same order number of parameters. Besides this, sparse
algorithm can lead us to find rational model size au- tomatically for certain
problem without concerning choosing an original model size.
