  With the increasing prevalence of voice-activated devices and applications,
keyword spotting (KWS) models enable users to interact with technology
hands-free, enhancing convenience and accessibility in various contexts.
Deploying KWS models on edge devices, such as smartphones and embedded systems,
offers significant benefits for real-time applications, privacy, and bandwidth
efficiency. However, these devices often possess limited computational power
and memory. This necessitates optimizing neural network models for efficiency
without significantly compromising their accuracy. To address these challenges,
we propose a novel keyword-spotting model based on sparse input representation
followed by a linear classifier. The model is four times faster than the
previous state-of-the-art edge device-compatible model with better accuracy. We
show that our method is also more robust in noisy environments while being
fast. Our code is available at: https://github.com/jsvir/sparknet.
