  Models based on attention mechanisms have shown unprecedented speech
recognition performance. However, they are computationally expensive and
unnecessarily complex for keyword spotting, a task targeted to small-footprint
devices. This work explores the application of Lambda networks, an alternative
framework for capturing long-range interactions without attention, for the
keyword spotting task. We propose a novel \textit{ResNet}-based model by
swapping the residual blocks by temporal Lambda layers. Furthermore, the
proposed architecture is built upon uni-dimensional temporal convolutions that
further reduce its complexity. The presented model does not only reach
state-of-the-art accuracies on the Google Speech Commands dataset, but it is
85% and 65% lighter than its Transformer-based (KWT) and convolutional (Res15)
counterparts while being up to 100 times faster. To the best of our knowledge,
this is the first attempt to explore the Lambda framework within the speech
domain and therefore, we unravel further research of new interfaces based on
this architecture.
