Voice-based Local Search Using a Language Model Look-ahead Structure for Efficient Pruning

On mobile terminals, voice-based local search services are quickly becoming a new important application. Voice search is essentially a large vocabulary speech recognition task with an open ended vocabulary, and this is a problem because speed and accuracy are essential for a good user experience. Fortunately when a user submits a local search query, contextual information such as the user´s current position can be used for constraining the full search space. In this paper, we use local information and present a pruning algorithm based on a LMLA (Language Model Look-Ahead) tree, which can significantly improve both the speed and the accuracy of the voice search system.