  On-device Virtual Assistants (VAs) powered by Automatic Speech Recognition
(ASR) require effective knowledge integration for the challenging entity-rich
query recognition. In this paper, we conduct an empirical study of modeling
strategies for server-side rescoring of spoken information domain queries using
various categories of Language Models (LMs) (N-gram word LMs, sub-word neural
LMs). We investigate the combination of on-device and server-side signals, and
demonstrate significant WER improvements of 23%-35% on various entity-centric
query subpopulations by integrating various server-side LMs compared to
performing ASR on-device only. We also perform a comparison between LMs trained
on domain data and a GPT-3 variant offered by OpenAI as a baseline.
Furthermore, we also show that model fusion of multiple server-side LMs trained
from scratch most effectively combines complementary strengths of each model
and integrates knowledge learned from domain-specific data to a VA ASR system.
