Improved recognition of contact names in voice commands

The recognition of contact names in mobile-device voice commands is a challenging problem. Some of the difficulties include potentially infinite vocabularies, low probability of contact tokens in the language model (LM), increased false triggering of contact voice commands when none are spoken, and very large and noisy contact name lists. In this paper we suggest solutions for each of these difficulties. We address low prior probability and out-of-vocabulary contact name problems by using class-based language models, and creating on-the-fly user dependent small language models containing only relevant names. These models are compiled dynamically based on analysis of the mobile device state. Since these solutions can increase biasing towards contact names during recognition, it is crucial to monitor false triggering. To properly balance this bias we introduce the concept of a contacts insertion reward. This reward is tuned using both positive and negative test sets. We show significant recognition performance improvements on data sets in three languages, without negatively impacting the overall system performance. The improvements are obtained in both offline evaluations as well as on live traffic experiments.