Improving vision-inspired keyword spotting using dynamic module skipping
  in streaming conformer encoder
Alexandre Bittar Paul Dixon Mohammad Samragh Kumari Nishu Devang Naik
Using a vision-inspired keyword spotting framework, we propose an
architecture with input-dependent dynamic depth capable of processing streaming
audio. Specifically, we extend a conformer encoder with trainable binary gates
that allow us to dynamically skip network modules according to the input audio.
Our approach improves detection and localization accuracy on continuous speech
using Librispeech top-1000 most frequent words while maintaining a small memory
footprint. The inclusion of gates also reduces the average amount of processing
without affecting the overall performance. These benefits are shown to be even
more pronounced using the Google speech commands dataset placed over background
noise where up to 97% of the processing is skipped on non-speech inputs,
therefore making our method particularly interesting for an always-on keyword
spotter.