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

Record Deduplication for Entity Distribution Modeling in ASR Transcripts