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On-device Virtual Assistants powered by Automated 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 (N-Gram word Language Models, 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.

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