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.