Improving Speech Recognition Accuracy of Local POI Using Geographical Models

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