Detecting retries of voice search queries

Abstract

When a system fails to correctly recognize a voice search query, the user will frequently retry the query, either by repeating it exactly or rephrasing it in an attempt to adapt to the system's failure. It is desirable to be able to identify queries as retries both offline, as a valuable quality signal, and online, as contextual information that can aid recognition. We present a method than can identify retries offline with 81% accuracy using similarity measures between two subsequent queries as well as system and user signals of recognition accuracy. The retry rate predicted by this method correlates significantly with a gold standard measure of accuracy, suggesting that it may be useful as an offline predictor of accuracy. © 2014 Association for Computational Linguistics.