  Nowadays voice search for points of interest (POI) is becoming increasingly
popular. However, speech recognition for local POI has remained to be a
challenge due to multi-dialect and massive POI. This paper improves speech
recognition accuracy for local POI from two aspects. Firstly, a geographic
acoustic model (Geo-AM) is proposed. The Geo-AM deals with multi-dialect
problem using dialect-specific input feature and dialect-specific top layer.
Secondly, a group of geo-specific language models (Geo-LMs) are integrated into
our speech recognition system to improve recognition accuracy of long tail and
homophone POI. During decoding, specific language models are selected on demand
according to users' geographic location. Experiments show that the proposed
Geo-AM achieves 6.5%$\sim$10.1% relative character error rate (CER) reduction
on an accent testset and the proposed Geo-AM and Geo-LM totally achieve over
18.7% relative CER reduction on Tencent Map task.
