  Automatic classification of speech commands has revolutionized human computer
interactions in robotic applications. However, employed recognition models
usually follow the methodology of deep learning with complicated networks which
are memory and energy hungry. So, there is a need to either squeeze these
complicated models or use more efficient light-weight models in order to be
able to implement the resulting classifiers on embedded devices. In this paper,
we pick the second approach and propose a network layer to enhance the speech
command recognition capability of a lightweight network and demonstrate the
result via experiments. The employed method borrows the ideas of Taylor
expansion and quadratic forms to construct a better representation of features
in both input and hidden layers. This richer representation results in
recognition accuracy improvement as shown by extensive experiments on Google
speech commands (GSC) and synthetic speech commands (SSC) datasets.
