End-to-end speech recognition accuracy metric for voice-search tasks

We introduce a novel metric for speech recognition success in voice search tasks, designed to reflect the impact of speech recognition errors on user's overall experience with the system. The computation of the metric is seeded using intuitive labels from human subjects and subsequently automated by replacing human annotations with a machine learning algorithm. The results show that search-based recognition accuracy is significantly higher than accuracy based on sentence error rate computation, and that the automated system is very successful in replicating human judgments regarding search quality results.