We explore the application of deep residual learning and dilated convolutions to the keyword spotting task, using the recently-released Google Speech Commands Dataset as our benchmark. Our best residual network (ResNet) implementation significantly outperforms Google's previous convolutional neural networks in terms of accuracy. By varying model depth and width, we can achieve compact models that also outperform previous small-footprint variants. To our knowledge, we are the first to examine these approaches for keyword spotting, and our results establish an open-source state-of-the-art reference to support the development of future speech-based interfaces.
Tang and Lin REF exploited residual architecture and dilated convolutions to achieve further improvement in accuracy while preserving compact models.
Deep Residual Learning for Small-Footprint Keyword Spotting
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Computer Science