Robust Keyword Spotting via Recycle-Pooling for Mobile Game

Shounan An, Youngsoo Kim, Hu Xu, Jinwoo Lee, Myungwoo Lee, Insoo Oh


We present an effective method to solve a small-footprint keyword spotting (KWS) task via deep neural network for mobile game. Our goal is to improve the accuracy of KWS in various environments. To this end, we propose a new neural network layer named recycle-pooling. Extensive experiments indicate that our recycle-pooling based convolutional neural network (RP-CNN) indeed improves the performance of KWS in both clean and noisy data for mobile game. We will perform live demonstration of RP-CNN based KWS integrated into a full-sized, production-quality mobile game A3: Still Alive, which is one of the major games from Netmarble this year and will be available on market soon.


Cite as: An, S., Kim, Y., Xu, H., Lee, J., Lee, M., Oh, I. (2019) Robust Keyword Spotting via Recycle-Pooling for Mobile Game. Proc. Interspeech 2019, 3661-3662.


@inproceedings{An2019,
  author={Shounan An and Youngsoo Kim and Hu Xu and Jinwoo Lee and Myungwoo Lee and Insoo Oh},
  title={{Robust Keyword Spotting via Recycle-Pooling for Mobile Game}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={3661--3662}
}