Geographic Language Models for Automatic Speech Recognition
Xiaoqiang Xiao, Hong Chen, Mark Zylak, Daniela Sosa, Suma Desu, Mahesh Krishnamoorthy, Daben Liu, Matthias Paulik
Abstract
In this paper, we propose improving automatic speech recognition (ASR) accuracy for local points of interest (POI) by leveraging a geo-specific language model (Geo-LM). Geographic regions are defined according to U.S. Census Bureau Combined Statistical Areas. Depending on the user's associated geographic region, for each user a class based Geo-LM is constructerd dynamically within a difference-LM based weighted finite state transducer (WFST) system. The benefits of this approach include: improved accuracy for local POI name recognition, flexibility in training, and efficient LM construction at runtime. Our experiments show that the proposed Geo-Lm achieves an average of over 18 % relative word error rate (WER) reduction on the tasks of local POI search, with no degradation to the general accuracy and very limited latency increase, compared to the baseline nationwide general LM. In addition to accuracy improvement, we also discuss optimization of runtime efficiency.
BibTeX
@inproceedings{icassp2018_geographiclangua,
title = {Geographic Language Models for Automatic Speech Recognition},
author = {Xiaoqiang Xiao and Hong Chen and Mark Zylak and Daniela Sosa and Suma Desu and Mahesh Krishnamoorthy and Daben Liu and Matthias Paulik and Yuchen Zhang},
booktitle = {ICASSP 2018},
year = {2018}
}