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.
Bibliographic reference. Chelba, Ciprian / Zhang, Xuedong / Hall, Keith (2015): "Geo-location for voice search language modeling", In INTERSPEECH-2015, 1438-1442.