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.
@article{arxiv.2311.01398,
title = {Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants},
author = {Youyuan Zhang and Sashank Gondala and Thiago Fraga-Silva and Christophe Van Gysel},
journal= {arXiv preprint arXiv:2311.01398},
year = {2023}
}