Abstract
The query distribution, in the speech recognition applications of directory assistance (DA) and voice-search, depends on the customer's location. This motivates the research on query models conditioned on the user location, here denoted as local models. We describe and test our methods for the estimation of local models with various degrees of spacial “granularity”, for the recognition of city-state (sub-task of DA) and for the recognition of business listings, spoken over iPhones in a nation-wide business-listing voice-search service. Our local language models improve the accuracy of city-state by 2.4% absolute (32% relative error reduction), and of voice-search by 2.2% (7% relative).
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