  Voice-enabled commercial products are ubiquitous, typically enabled by
lightweight on-device keyword spotting (KWS) and full automatic speech
recognition (ASR) in the cloud. ASR systems require significant computational
resources in training and for inference, not to mention copious amounts of
annotated speech data. KWS systems, on the other hand, are less
resource-intensive but have limited capabilities. On the Comcast Xfinity X1
entertainment platform, we explore a middle ground between ASR and KWS: We
introduce a novel, resource-efficient neural network for voice query
recognition that is much more accurate than state-of-the-art CNNs for KWS, yet
can be easily trained and deployed with limited resources. On an evaluation
dataset representing the top 200 voice queries, we achieve a low false alarm
rate of 1% and a query error rate of 6%. Our model performs inference 8.24x
faster than the current ASR system.
