  Recent models based on attention mechanisms have shown unprecedented
performance in the speech recognition domain. These are computational expensive
and unnecessarily complex for the keyword spotting task where its main usage is
in small-footprint devices. This work explores the application of the Lambda
networks, a framework for capturing long-range interactions, within this
spotting task. The proposed architecture is inspired by current
state-of-the-art models for keyword spotting built on residual connections. Our
main contribution consists on swapping the residual blocks by temporal Lambda
layers thus bypassing the expensive computation of attention maps, largely
reducing the model complexity. Furthermore, the proposed Lambda network is
built upon uni-dimensional convolutions which also dramatically decreases the
number of floating point operations performed along the inference stage. This
architecture does not only reach state-of-the-art accuracies on the Google
Speech Commands dataset, but it is 85% and 65% lighter than its multi headed
attention (MHAtt-RNN) and residual convolutional (Res15) counterparts, while
being up to 100x faster than them. To the best of our knowledge, this is the
first attempt to examine the Lambda framework within the speech domain and
therefore, we unravel further research and development of future speech
interfaces based on this architecture.
