  This paper proposes a Sub-band Convolutional Neural Network for spoken term
classification. Convolutional neural networks (CNNs) have proven to be very
effective in acoustic applications such as spoken term classification, keyword
spotting, speaker identification, acoustic event detection, etc. Unlike
applications in computer vision, the spatial invariance property of 2D
convolutional kernels does not fit acoustic applications well since the meaning
of a specific 2D kernel varies a lot along the feature axis in an input feature
map. We propose a sub-band CNN architecture to apply different convolutional
kernels on each feature sub-band, which makes the overall computation more
efficient. Experimental results show that the computational efficiency brought
by sub-band CNN is more beneficial for small-footprint models. Compared to a
baseline full band CNN for spoken term classification on a publicly available
Speech Commands dataset, the proposed sub-band CNN architecture reduces the
computation by 39.7% on commands classification, and 49.3% on digits
classification with accuracy maintained.
