Geo-Centric Language Models for Local Business Voice Search

Voice search is increasingly popular, especially for local business directory assistance. However, speech recognition accuracy on business listing names is still low, leading to user frustration. In this paper, we present a new algorithm for geo-centric language model generation for local business voice search for mobile users. Our algorithm has several advantages: it provides a language model for any user in any location; the geographic area covered by the language model is adapted to the local business density, giving high recognition accuracy; and the language models can be pre-compiled, giving fast recognition time. In an experiment using spoken business listing name queries from a business directory assistance service, we achieve a 16.8% absolute improvement in recognition accuracy and a 3-fold speedup in recognition time with geocentric language models when compared with a nationwide language model.