  Keyword Spotting (KWS) plays a vital role in human-computer interaction for
smart on-device terminals and service robots. It remains challenging to achieve
the trade-off between small footprint and high accuracy for KWS task. In this
paper, we explore the application of multi-scale temporal modeling to the
small-footprint keyword spotting task. We propose a multi-branch temporal
convolution module (MTConv), a CNN block consisting of multiple temporal
convolution filters with different kernel sizes, which enriches temporal
feature space. Besides, taking advantage of temporal and depthwise convolution,
a temporal efficient neural network (TENet) is designed for KWS system. Based
on the purposed model, we replace standard temporal convolution layers with
MTConvs that can be trained for better performance. While at the inference
stage, the MTConv can be equivalently converted to the base convolution
architecture, so that no extra parameters and computational costs are added
compared to the base model. The results on Google Speech Command Dataset show
that one of our models trained with MTConv performs the accuracy of 96.8% with
only 100K parameters.
