  Voice digital assistants must keep up with trending search queries. We rely
on a speech recognition model using contextual biasing with a rapidly updated
set of entities, instead of frequent model retraining, to keep up with trends.
There are several challenges with this approach: (1) the entity set must be
frequently reconstructed, (2) the entity set is of limited size due to latency
and accuracy trade-offs, and (3) finding the true entity distribution for
biasing is complicated by ASR misrecognition. We address these challenges and
define an entity set by modeling customers true requested entity distribution
from ASR output in production using record deduplication, a technique from the
field of entity resolution. Record deduplication resolves or deduplicates
coreferences, including misrecognitions, of the same latent entity. Our method
successfully retrieves 95% of misrecognized entities and when used for
contextual biasing shows an estimated 5% relative word error rate reduction.

Computation and Language