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Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants. (arXiv:2311.01398v1 [cs.CL])
cs.CL updates on arXiv.org arxiv.org
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 …
arxiv asr assistants automatic speech recognition domain information integration knowledge language language models modeling paper query recognition server speech speech recognition spoken strategies study virtual virtual assistants word