Semantic data selection for vertical business voice search

Local business voice search is a popular application for mobile phones, where hands-free interaction and speed are critical to users. However, speech recognition accuracy is still not satisfactory when the number of businesses and locations is extended nationwide. For mobile users, searching a local business directory is often related to the fulfillment of specific tasks "on-the-move", such as finding a restaurant, a movie theater, or a retailer chain. Restricting the local search to specific domains improves the quality of search results. In this paper, we present a new approach to data selection for bootstrapping and optimizing language models for vertical business sectors by exploiting semantic knowledge encoded in the business database and in the business category taxonomy. We demonstrate that, in the case of queries in the restaurant domain and without collecting new data, speech recognition word accuracy improves by 9.5% relative when compared with a generic local business language model.