  Used for simple commands recognition on devices from smart routers to mobile
phones, keyword spotting systems are everywhere. Ubiquitous as well are web
applications, which have grown in popularity and complexity over the last
decade with significant improvements in usability under cross-platform
conditions. However, despite their obvious advantage in natural language
interaction, voice-enabled web applications are still far and few between. In
this work, we attempt to bridge this gap by bringing keyword spotting
capabilities directly into the browser. To our knowledge, we are the first to
demonstrate a fully-functional implementation of convolutional neural networks
in pure JavaScript that runs in any standards-compliant browser. We also apply
network slimming, a model compression technique, to explore the
accuracy-efficiency tradeoffs, reporting latency measurements on a range of
devices and software. Overall, our robust, cross-device implementation for
keyword spotting realizes a new paradigm for serving neural network
applications, and one of our slim models reduces latency by 66% with a minimal
decrease in accuracy of 4% from 94% to 90%.
