Using query performance predictors to improve spoken queries

Abstract

Query performance predictors estimate a query’s retrieval effectiveness without user feedback. We evaluate the usefulness of preand post-retrieval performance predictors for two tasks associated with speech-enabled search: (1) predicting the most effective query transcription from the recognition system’s n-best hypotheses and (2) predicting when to ask the user for a spoken query reformulation. We use machine learning to combine a wide range of query performance predictors as features and evaluate on 5,000 spoken queries collected using a crowdsourced study. Our results suggest that pre-and post-retrieval features are useful for both tasks, and that post-retrieval features are slightly better.