Geo-Location For Voice Search Language Modeling

We investigate the benefit of augmenting with geo-location information the language model used in speech recognition for voice-search.We observe reductions in perplexity of up to 15% relative on test sets obtained from both typed query data, as well as transcribed voice search data; on a subset of the test data consisting of "local" queries - search results displaying a restaurant, some address, or similar - the reduction in perplexity is even higher, up to 30% relative.Automatic speech recognition experiments confirm the utility of geo-location information for improved language modeling. Significant reductions in word error rate are observed both on general voice search traffic, as well as "local" traffic, up to 2% and 8% relative, respectively.