Robust location understanding in spoken dialog systems using intersections

The availability of digital maps and mapping software has led to significant growth in location-based software and services. To safely use these applications in mobile and automotive scenarios, users must be able to input precise locations using speech. In this paper, we propose a novel method for location understanding based on spoken intersections. The proposed approach utilizes a rich, automatically-generated grammar for street names that maps all street name variations into a single canonical semantic representation. This representation is then transformed to a sequence of position-dependent subword units. This sequence is used by a classifier based on the vector space model to reliably recognize spoken intersections in the presence of recognition errors and incomplete street names. The efficacy of the proposed approach is demonstrated using data collected from users of a deployed spoken dialog system.